Category: Skin Cancer

Why do we use sunscreen? To save ourselves from harmful sun rays, of course. Did you know that these harmful rays, aka UV rays of the sun, are the major reason of causing skin cancer? However, despite the UV rays, are there any other factors that can lead to skin cancer? Of course, there are. Genes are just one such factor to name. The science of genetics is far more complicated and far fetched from what a human mind can contain. But, from what is proven by science, genetic bias is something that cannot be ruled out in any disease.

Faulty genes

We inherit a pair of genes from both our parents. These genes decide everything that goes in our bodies. From the color of our hair, to eyes, to facial features, and even intellect, everything is coded as genes. A lot of times, when parents pass on a faulty gene, it automatically increases the likelihood of developing that particular faulty condition.

Am I at risk?

You might find yourself asking this question. The answer is loud and clear. If you have a family history of skin cancer, you are at risk. However, how do you know if you have a family history? Here’s how you can find out:

• If you have 2 or more close relatives on the same side of your family who had skin cancer, you are at risk.
• The age of the relative when he/she developed the cancer is also very crucial. A later age does not prove much of a risk. However, if your relative has developed the cancer before 50, you might be at high risk too.
• You might also be at risk if you or your close family relatives have 2 or more unusual looking moles anywhere on your body. These are called spitz nevi, and they might grow to be malignant (i.e. they may spread to other areas of the body).
• In our skin we have a substance called melanin. The function of melanin is to give us our skin color, and protection from the sun. Since darker skin contains more melanin, it automatically means that dark people have more of this natural cancer-fighting chemical. So if your family has a light skin tone, you can also be at risk.

What to do?

Once you find out where you stand at the risk percentage, you need to take care of a few things. They say, prevention is better than cure. Here’s how you can prevent yourself:

• Consult your physician and inform him/her about your genetic likelihood. Your doctor might advise you on screening tests every now and then.
• Keep a track of changes in your body. If you see any new moles, or the old ones changing shape or color, make sure you inform your physician immediately.
• Wear sunscreen whenever you are stepping out in the sun. Make sure sure sunscreen is SPF 15 or more.
• Wear protective clothing, with full sleeves and pants when you have to step out in the sun.
• Go for self tanning products instead of tanning beds or sunbaths.
• Consider living at places with a colder climate.

Cancer is a type of tumour that forms when the protooncogene cells in the human body are converted to an oncogene. The oncogene cells replicate rapidly giving rise to a malignant tumour. Due to their speedy replication action, the infected cell and tissue materials spread into the other parts of the body if undetected. Cancer that occurs on the skin of humans is known as Skin Cancer. These may occur in the form of lesions, black spots or coloured patches on the skin.

Skin cancer could occur due to various reasons. Exposure to UV light or sources of radiations could cause skin cancers. Due to the increase in atmospheric pollution and thinning of the ozone layer, UV rays from the sun directly comes in contact with the human body. The UV light could penetrate clothing materials. So wearing dark sleeved clothes doesn’t always help us from preventing skin cancer. UV protecting sunscreens doesn’t always act as a barrier against UV lights and the medical professionals at SunDoctors agree to that. However certain Reputed and Costly brands of sunscreen provide somewhat of protection.

Treatment of cancer could be costly and painful. If detected at early stages the medical professionals would remove the lesion or tumour from the body and no other harm would occur. But in late stages, it could be quite difficult to treat. Today for skin cancer treatment a variety of procedures are available. An abundance of natural resources for medicinal use exist worldwide, of which many have not yet been exploited for possible application in the pharmaceutical industry. Over 50% of all accessible drugs in the market originate from natural sources, of which over 70% of anti-cancer drugs have their origin in natural sources. Natural sources include plants, animals, microbes and marine life. Plants are the most utilized natural resource for applications in the pharmaceutical science and still comprise the leading natural source for new drugs and lead compounds, due to their accessibility and abundance. To date, only a few naturally derived drugs exist on the market that targets skin-related cancers, whereas none have yet been approved for topical application. This could be accredited to the known side effects of these agents when topically applied to the skin. Using those or the following medical procedures could help reduce the effects of skin cancer to a certain level.

1. Simple excision: – the medical professional would excise or slice away the tumour or lesion formed on the skin and would treat you with various types of anti-cancer drugs for further formation of tumours in the future.
2. Mohs microscopic Surgery: – the skin layers are thinly sliced away and after each cut, the skin is observed with a microscope to ensure no cancer cells are left behind.
3. Cryosurgery: – the surface of the skin affected with cancer is subjected to extremely low cold temperatures to freeze and destroy the cancer cells.
4. Radiation Therapy: – this type of treatment includes exposing the affected area of the skin to X-Rays or Gamma rays to destroy the cancer cells. Internal cancers are treated by inserting wires, catheters or needles inside the tumour destroying it.
5. Chemotherapy: – This is probably the most common way to treat cancer. For Skin Cancer treatment, chemical ointment (such as Topical fluorouracil) is used. As the cancer isn’t located in an inaccessible internal organ, thus ointment is used.

Cancer in itself is a deadly disease affecting thousands of people all over the world and it is due ti high amount of radiation present on the Earth surface.

Introduction

The research paper ‘Dermatologist-level classification of skin cancer with deep neural networks” proposes that mobile devices combined with artificial intelligence have the potential of providing “low-cost universal access to vital diagnostic care.” This means that there is a rise in technology to enable medical diagnosis in an effective and inexpensive way. To support their claim, the authors utilized a single convolutional neural network (CNN) to predict skin cancer diagnosis by classifying skin lesion images into different skin diseases. The CNN model is meant to mimic human brains by training it with large datasets so that it learns from patterns in the data. The authors’ goal was to show readers a real-life and applicable study that proves that artificial intelligence could reach a higher objective. Furthermore, the higher purpose of this algorithm is to track skin lesion and detect skin cancer at an earlier stage, giving patients a longer survival rate than those who are diagnosed at later stages. This study introduces CNNs to a function that could potentially save lives. Finally, more research should be considered in order to fine tune the algorithm and scale it to several medical entities.

Background

In this study the authors decided to focus on skin cancer detection because of its high death rate. Every year, 5.4 million new cases of skin cancer are diagnosed in the United States. Melanomas, which is the strongest type of skin cancer, represents 5% of all skin cancers, but “they account for approximately 75% of all skin-cancer-related deaths, and are responsible for over 10,000 deaths annually in the United States alone.” Patients diagnosed at an early stage with melanoma are estimated a 5-year survival rate, when diagnosed at a later stage this rate can drop to about 14%; therefore, detecting skin cancer at an early stage is critical for patients. The data driven method utilized by the authors allows for medical practitioners and patients to proactively track skin lesions and as a result detect skin cancer earlier.

For many years the process for detecting skin cancer has been done through visual diagnosis, followed by a dermoscopic analysis, biopsy, and a histopathological examination. Visual diagnosis has many problems attached to it, not only are some skin lesions difficult to identify as cancerous through the naked eye, but also it requires a certain level of expertise. Dermoscopy analysis and histopathological examination have proven to be far better than visual inspection due to standardized imagery; however, they still require an expert to differentiate skin lesions. The technique proposed by the authors of this research paper has shown advances in other areas involving visual analysis such as playing Atari games or strategic board games and has even performed better than humans. This method applied to skin cancer diagnosis has the ability to be scalable and overcome variability in images, which make classification more challenging, by training and validating 1.41 million images that create a robust classification algorithm.

Research Design and Methods

In general, to obtain a high accuracy when testing a machine learning model, one must first train the model to find patterns in the data then apply the model to never seen data, called test data. In this study, the data consisted of 129,450 images, of those 127,463 were labelled by dermatologists and were selected to train and validate the model. The remaining 1,942 images were labelled by biopsy results and were destined to test the model. The images labelled by dermatologists were not necessarily confirmed by a biopsy. Therefore, the accuracy of the training and validation is not reliable since dermatologists could have mistakenly categorized a skin lesion as cancerous when it was not, yielding to false negatives. On the other hand, it is safe to say that the biopsy-labelled images were correctly labelled, hence why they were used to test the CNN model. Although the dermatologist labelled data set is somewhat unreliable, it serves a good purpose to find patterns in the data.

It demonstrates the high-level deep CNN process for classifying skin images. Essentially, in each layer the model is detecting similarities by performing regression and learning the correlations between the labels previously stated in the data. The skin image is then classified into 757 fine-grained training classes that yield a probability distribution over these classes. This means that the model outputs the weight of each training class obtained from the skin image. Afterwards, the algorithm allots images to coarser inference classes such as malignant melanocytic lesion, which could be conformed of amelanotic melanoma and acrolentiginous melanoma. The inference classes are accompanied by a probability metric which is calculated by adding the probabilities of the training classes corresponding to the inference class.

This process not only breaks down the images into more digestible classes, but also outputs a tangible metric giving the model the ability to categorize an image with a level of importance and a corresponding accuracy. This is especially important because medical practitioners, when diagnosing, can express the likelihood that a skin lesion is malignant, benign, or non-neoplastic and further assess its composition. Suddenly the original black and white process has more layers. Here is it being showcased the intricacy of the model and its ability to measure results.

The neural network follows a nine-fold cross-validation, this means that the validation dataset is randomly pooled nine times. The idea behind cross-validation is to test the trained model and assess its accuracy before it is tested with unseen data. The CNN in this study was validated in two ways, first, it was partitioned into three categories: benign, malignant, and non-neoplastic lesions, reference first-level nodes. Here the CNN model reached an accuracy of 72.1 +/- 0.9% (mean +/- s.d.), against two dermatologist who acquired an accuracy of 65.56% and 66.00% on a subset of the data. Second, it was classified into nine disease options which followed similar medical treatment plans, reference second-level nodes. In this task, the CNN yields an accuracy of 55.4 +/- 7% against the same two dermatologist who achieved an accuracy of 53.3% and 55.0%. This means that the CNN performs better when trained on finer disease partition and overall better than dermatologists. Though, it is important to note that the main idea behind this analysis is to prove that the taxonomy of skin disease is effective, rather than to compare metrics. In fact, more than showing performance, the accuracy percentages indicate “that the CNN is learning relevant information.” With this, the authors prove the value of training models as a vital step to continuing the path of artificial intelligence in medical diagnosis.

Finally, the CNN model was measured utilizing a subset of the test data to have more specific results. The researchers focused on keratinocyte carcinoma and melanoma classification. They utilized two metrics to measure success, sensitivity which refers to the true positive rate (TPR) and specificity which is the true negative rate (TNR). The results were plotted into a receiver operating characteristic curve, known as ROC curve, which captures the trade-off between the TPR and TNR as the discrimination threshold varies. The blue line is the ROC curve, the red dots represent a single dermatologist sensitivity and specificity. Those dots under the curve underperform the CNN, which are the majority. Lastly, the green dot is the average of all dermatologists. This is a pictorial representation that the algorithm studied performs better than experts with never seen data. This test starts conversations about the future success of this model if more data is included and it is scaled to other medical areas.

Conclusions

The authors were proposing a deep learning model for classifying skin lesions into different classes that would revolutionize the skin cancer diagnosis and potentially expand to other medical areas. They based their proposal on similar acting models and data that had performed better than humans. Such models were applied to image recognition in games. Because of the success in other areas they saw opportunity to apply this technique to the medical world. By utilizing a large data set of images, they created a robust classifying model that has the potential to scale and expand to different medical areas. Throughout the research paper, the authors set up a strong case to diagnose skin cancer using CNN by showing that the more reliable data in conjunction with a well-trained model, has the ability to achieve same or better results than experts.

Moreover, this research paper showed that this technique has the potential to diagnose skin cancer at an early stage which could be the difference between a 5-year survival rate versus 8.5 months. The study proved that the process could be as good or better than the current skin cancer diagnosis by comparing metrics achieved by the model against dermatologists’ performance. Furthermore, it also showed that the CNN model has the ability to strengthen and scale to other settings using the appropriate set of data.

Discussion

This study overall showed a strong case for the expanded use of deep neural networks in dermatology. However, there are some future steps to take in order to better apply this method. Since the testing section was performed using a subset of the data which referred to keratinocyte carcinoma and melanoma classification, the authors acknowledged the need for further research in a real-world setting. This would enable them to confirm that this procedure is scalable to all skin lesions and potentially other clinical areas. It is also important to note that the authors recognize that skin cancer diagnosis is not solely based on skin lesion identification. Though, the fact that this model performed equally as good as dermatologist experts shows the potential for a data driven diagnosis approach to grow medical care access. In addition, the main constraint for this technique is the amount of data obtain, as proven above. Therefore, there is still a continuous need to gather more data. Additionally, if this model was to expand to other medical areas research should be considered to find reliable data.

Furthermore, it is lightly mentioned that this model could be easily included in mobile devices, however, there was no prove or test performed in mobile devices. Another consideration for this research would be to include the use of mobile devices and compare its performance through this technology. As this could potentially impact the results in a real-world setting if the experience is not user-friendly.

References

1. Esteva, Andre and Kuprel, Brett and Novoa, Roberto A. and Ko, Justin and Swetter, Susan M. and Blau, Helen M. and Thrun, Sebastian. Dermatologist-level classification of skin cancer with deep neural networks. Nature, vol. 542, nature21056 (2017).
2. Masood, A. & Al-Jumaily, A. A. Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int. J. Biomed. Imaging 2013, 323268 (2013).

Research has shown that one of the main biological factors for an individual for the development of skin cancer is that of their heritage, being of European decent. The level of pigmentation in a person’s skin has a direct affect on the possibility of being diagnosed with skin cancer .

Many Australians still hold the belief that having a tan is healthy2,6. A tan will offer some form of sun protection but it is only equivalent to using a sunscreen with an SPF 3.2 Cognitive

A number of groups continue to be at the forefront of skin cancer prevention programs and campaigns including children, young people and outdoor workers. In 2012, more than 50% of adult Queenslanders reported being sunburnt in the previous year. Highest rates occurred in those aged 16–24 years with over 70% reporting sunburn, decreasing to 11% in the age group 75 years and older.

Sunlight exposure at high levels in childhood is a strong determinant of risk of skin cancer and melanoma. As most sun exposure occurs before adulthood, it is important to protect the skin of infants, children and adolescents. Around 40% of Australian children under 4 years attended formal care in 2011-14 so a sun safe environment in these services can play a major role in minimising children’s exposure to UV and can also influence the child’s long term sun protective behaviours.

Schools are another area for addressing sun safety. A comprehensive, coordinated approach is required to incorporate a supportive physical and social environment, including policy and curriculum, underpinned by shared responsibility and contributions from school management and staff, students and parents and the broader community. While knowledge of sun protective behaviours is generally high, translating knowledge into behaviour change of secondary school students is complex and the identification of innovative solutions to educate and empower students to make sun safe choices is necessary.

Outdoor workers in Queensland are exposed to a UV index of three or more (requiring sun protection) all year round and are at high risk of harm from sun exposure. Over 1.2 million workers in Australia are regularly exposed to excessive UV radiation during their working day. Occupational exposure to UV radiation is a modifiable risk factor and the potential for change in workplace settings is large. Implementation of targeted interventions that are multi-strategic and use healthy public policy approaches may be successful in reducing unsafe sun exposure in the workplace.

In conclusion, this presentation has looked at the determinants related to skin cancer and have been presented using the Red Lotus health model. We thank you for your time.

Abstract— Skin lesions are significant in determining dermatological medical conditions globally. Early diagnosis of malignant melanoma by dermoscopy imaging considerably will increase the survival rate. Throughout this paper, we tend to present deep learning-based approaches to resolve 2 issues in skin lesion analysis employing a dermoscopic image containing the tumor. Estimation of these biomarkers are wont to give some insight, whereas detecting cancerous cells and classifying the lesion as either benign or malignant. This paper presents groundwork for the detection of skin lesions with cancerous inclination by segmentation and subsequent application of Convolution Neural Network on dermoscopy pictures. The proposed models are trained and evaluated on normal benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2016 challenge, which consists of 2000 training samples and 600 testing samples. pictures with skin lesions were segmental based mostly on individual channel intensity thresholding. The resultant pictures were fed into CNN for feature extraction. The extracted features were then used for classification by associate ANN classifier. Previously, many approaches are used for subject diagnostic with variable degree of success. As compared to a previous best of ninety-seven, the methodology presented in this paper yielded associate accuracy of ninety eight.32%.

Keywords— skin lesion, segmentation, convolutional, neural network (CNN), artificial neural network (ANN), ReLU, sensitivity, specificity, accuracy

I. Introduction

Melanoma could be a kind of skin cancer that has verified itself to be quite fatal. It’s the reason behind seventy-five of the deaths caused by skin connected diseases, and these numbers are becoming worse with the passage of your time [1]. It’s annually calculable that five million lives are suffering from skin cancer within the U.S [2, 3], and 9,000 lives are annually claimed by skin cancer making it a serious threat and cause for rising concern [3]. Malignant melanoma diagnosed in early stages is the necessary key to prevent this disease, otherwise, it becomes life-threatening if not cured in early stages. This characteristic of cancer emphasizes on the importance of early and correct diagnosing of skin cancer. Though malignant melanoma cancer shows visual symptoms on skin and can be known by dermatologist, rather than using expensive large machine depends on their expertise and an inexperienced dermatologist will confuse melanoma with scars or non-lethal disease of the skin [4]. Another side to the problem statement is the increasing population of world and fewer dermatologist per capita to deal with this problem. Increasing the ratio of experienced dermatologists per capita may be a far-fetched task and compared to, that introducing an automatic software-based technique to help during this war against skin cancer looks a lot of viable [5].

As pigmented lesions occurring on the surface of the skin, melanoma is amenable to early detection by expert visual inspection. It is also amenable to automated detection with image analysis. Given the widespread availability of high-resolution cameras, algorithms that can improve our ability to screen and detect troublesome lesions can be of great value. Dermoscopy is an imaging technique that eliminates the surface reflection of skin. By removing surface reflection, visualization of deeper levels of skin is enhanced. Prior research has shown that when used by expert dermatologists, dermoscopy provides improved diagnostic accuracy, in comparison to standard photography. Dermoscopy image is a different methodology to simply examine skin diseases. The skin images are enlarged and lit within the affected region of skin so as to make sure clarity and discard any skin reflection [6]. However, lack of progress during this methodology and this method depends on human vision and experience to detect disease, introducing a person’s error that has LED to poor efficiency in malignant melanoma detection. Efficiency of dermoscopy imagination technique may be improved by adding an automatic tool to identify the anomaly of skin.[7, 8].to enhance dermoscopy image, there are several works tried to boost the effectiveness of image segmentation technique. Multispectral imaging and confocal microcopy are wide used to address the issue of malignant melanoma detection. These machines are very expensive and large. Also, special training is needed to utilize this method. Significantly, well- trained and experienced dermatologists will yield higher results from this methodology [9].

Segmentation of affected skin image could be a crucial method for many detection algorithms. Accurate segmentation is the primary key for getting high accuracy of succeeding steps within the process. Many works are tried to extract lesion portion from the images. Garnavi et al. worked on segmentation of pictures using optimal color channels and hybrid thresholding technique for skin lesion analysis [10]. Schaefer developed segmentation on the pictures of lesion space by auto border detection technique [11] and extracted features (i.e. color, shape, and texture, were used for detection of melanoma [12]. Codella et al. combined support vector machine (SVM), and convolution neural network (CNN) for identification of skin cancer [13]. The planned methodology works on the combination of automated segmentation and CNN module. The presented methodology focuses on up segmentation of an image, so applying CNN technique specifically for enhance skin cancer detection. We use 3 totally different strategies of automatic segmentation supported thresholding, morphology functions and active contours. In the proposed system, segmentation and classification of skin lesion as cancerous or normal based on the texture features. The proposed segmentation framework is tested by comparison lesion segmentation results and malignant melanoma classification results to results using different state-of-art algorithms. The proposed framework has higher segmentation accuracy compared to all or any different tested algorithms.

Figure 1. Implemented system flow.

II. Dataset

Dataset used for this study has been obtained from International Skin Imaging Collaboration (ISIC) [14]. 900 images (1024×767 pixels) acquired from ISIC 2016 were used for training. Further for training, dataset of 379 images were short-listed and were labeled as training images. These images were additionally classified on the idea of their characteristics into 3 varieties, Melanoma, Seborrheic skin disorder, and Nevus. skin cancer is associate degree image containing symptoms of melanoma cancer and we have a tendency to classify it in malignant class, whereas keratosis is a picture of non-lethal skin disorder, and birthmark is a picture of birthmark. The last 2 pictures are listed in benign class. Classifying of malignant and benign will facilitate skin doctor to change and help deduction of the results.

Figure 2. Dataset

Figure 2: Examples of lesion images from ISIC 2016 and their masks. The first row shows the original images of different lesions. The second row shows the segmentation masks. The third row shows the superpixel mask for dermoscopic feature extraction. The scales for the lesion images are 1022 pixels × 767 pixels, 3008 pixels × 2000 pixels and 1504 pixels × 1129 pixels, respectively.

III. Proposed Methodology

Automated tools for image processing were used to tackle the given problem. These tools generally works in following steps.

1. Accurate Segmentation
2. Feature Extraction
3. Classification of Lesion

The flow of overall methodology is shown in Fig. 1. The images were accurately segmented for subsequent steps, and CNN was then used for feature extraction and classification. CNN can be divided into two categories: convolution layer which extract features, and ANN classifier which classifies an image. These steps will be discussed in detail in the following sections.

A. Segmentation

The dataset contains multiple pictures of malignant and benign pigmented skin lesions. A pigmented skin lesion, once stated in dermatoscopy, could be a tiny abnormal space on skin that is typically darker tone and incorporates a distinguishable texture on the image, compared to the image of traditional skin. Generalized statistical distribution (GGD) is that the technique that is being used for image segmentation. All coaching images, were divided into its R, G and B color channels to one by one confirm the extent of involvement in a malignant.

Intensities (I) of the malignant space was obtained, and GGD model was established. Initially, 900 coaching pictures were computed by equation one and a couple of for acquire GGD model.

TABLE I GGD STATISTICS.

Channel

Mean(µ)

Standard Deviation(σ)

Red

145.028

27.3

Green

104.53

28.77

Blue

80.36

22.68

Table one shows the datum results for developing GGD model. These values were used to substitute in (3), then Generalized normal distribution (GGD) will be obtained. Fig. two shows the distribution of GGD models of R, G and B channels.

(3) Figure 3. Proposed GGD model.

After applying GGD model on an picture, morphological operation has to be performed to get rid of unwanted components. Given the actual fact that the image of skin lesions is darker in tone color, compared to the image of traditional skin around it. Mean and standard deviation of the latter known, a generated mask should satisfy equation (4) (4) µ − I ≥ σ

1.1. Pre-Processing

The original training set contains 2000 skin lesion images of different resolutions. The resolutions of some lesion images are above 1000 × 700, which require a high cost of computation. It is necessary to rescale the lesion images for the deep learning network. As directly resizing images may distort the shape of the skin lesion, we first cropped the center area of lesion image and then proportionally resize the area to a lower resolution. The size of the center square was set to be 0.8 of the height of the image, and automatically cropped with reference to the image center. This approach not only enlarges the lesion area for feature detection, but also maintains the shape of the skin lesion.

1.2. Data Augmentation

The dataset contains three categories of skin lesion, i.e., Melanoma, Seborrheic keratosis and Nevus. As the number of images of different categories varies widely, we accordingly rotated the images belonging to different categories.

B. Convolution Neural Networks

CNN has established to be quite in in classification issues of pictures. it’s a superb tool for learning native and world information by combining easy options like edges and curves to convey a lot of complicated features like corners and shapes. CNN was enforced for detective work malignant melanoma cancer. Since the image of malignant melanoma cancer has no distinct feature, therefore, deep layer CNN cannot perform well for malignant melanoma cancer detection due to overfitting drawback. This drawback arises once the model is trained too well. Consequently, it starts to own harmful result on the results. it’s steered that CNN design is a lot of appropriate for distinguishing texture- primarily based pictures and it will avoid overfitting issues.

CNN Architecture : the layout of the network employed in this study is illustrated in Fig. 3. RGB channel input of skin image was normalized with zero mean and unit variance. This normalized matrix was fed into the convolution layer. Convolution layer is that the 1st layer that convolves sixteen totally different kernel of 7×7 pixels to provide 16 different output channels. The extracted feature channels were fed into pooling layer for reducing the dimension of these channels, or it may be referred as sampling. These sampled channels were used as inputs for the following layers, referred to as absolutely connected layers.

We used three-layer connected model for image classification. Every consequent layer will cut back the amount of connected neurons (i.e. 100, 50, five respectively). In distinction to the DCNN, we tend to have used single convolution layer since there are few options to be learned, thus it will cut back the complexness of the CNN and avoid overfitting downside. Summary of every layer of CNN is mentioned as follows

Skin Lesions Based on Convolutional Neural Networks:

In this section, the individual CNN methods used to classify skin lesions are presented. CNNs can be used to classify skin lesions in two fundamentally different ways. On the one hand, a CNN pre trained on another large dataset, such as ImageNet, can be applied as a feature extractor. In this case, classification is performed by another classifier, such as k-nearest neighbors, support vector machines, or artificial neural networks. On the other hand, a CNN can directly learn the relationship between the raw pixel data and the class labels through end-to-end learning. In contrast with the classical workflow typically applied in machine learning, feature extraction becomes an integral part of classification and is no longer considered as a separate, independent processing step. If the CNN is trained by end-to-end learning, the research can be additionally divided into two different approaches: A basic requirement for the successful training of deep CNN models is that sufficient training data labeled with the classes are available. Otherwise, there is a risk of overfitting the neural network and, as a consequence, an inadequate generalization property of the network for unknown input data. There is a very limited amount of data publicly available for the classification of skin lesions.

2) Convolution Based Feature Extraction:

Convolution layer is that the most significant layer in CNN and was ordinarily used for feature extraction from the image. One or quite one 2nd channels were treated as inputs to the convolution layers. These channels were convolved with completely different kernels. Every kernel has its own weights and represents a neighborhood feature extractor. Kernel is employed to extract output options that will or might not match with the dimension of the inputs. The feature outputs

Figure 4. Segmentation methodology: illustrates the proposed process for the segmentation of images.

contain the needed options of the input image. Pooling layer plays a necessary role in reducing the scale of options. We are applied pooling layer with the kernel of 2×2 pixels. This kernel down samples the input by choosing most worth from each consecutive 2×2 pixels of the inputs. These output channels can have 1/2 the samples, and our computation can become easier. Fully-connected layers incorporates somatic cells that connect every neuron from previous layers to each neuron within the next layer. This approach we tend to deduced results since every somatic cell is connected to each end in previous layer, we tend to get collective assessment of each feature extracted from the image.

3) ANN Classification: CCN will not need any further classifier like SVM, KNN since three fully-connected layers were used for coaching the classification model. Three- layer ANN classifier was employed in our methodology. This sort of classification brings its own distinctive advantages, like it is feasible to use a back-propagation algorithmic program, that adjusts the parameters of neurons in all layers to get higher classification model. For nerve cell activation, nonlinear functions were employed in ANN [15].

In this CNN model, non-linear ReLU was used as activation operate. ReLU could be a straightforward operate as shown in equation (5). Compared to alternative activation functions like Sigmoids and tanh, ReLU doesn’t have gradient vanishing drawback, that is a very important issue to contemplate a gradient dependent machine learning method, like enforced by this study. corrected linear operate (ReLU) due to its simplicity and gradient pre-service improves each learning speed and performance of our CNN by two.3. CNNs are neural networks with a specific architecture that have been shown to be very powerful in areas such as image recognition and classification.

IV. Results

The performance of planned methodology were evaluated in terms of sensitivity (6), specificity (7), and accuracy (8). Testing dataset of ISIC 2016 was used for this purpose, where 379 pictures were obtainable for testing.

Where,

TP = True Positive TN = True Negative

COMPARISON WITH LATEST OTHER TECHNIQUES

Algorithm

[16]

[17]

[18]

Proposed

Sensitivity

85.71

96

–

98.15

Specificity

85.71

96

–

98.41

Accuracy

82

97

85

98.32

FN = False Negative FP = False Positive

Figure 5. Proposed CNN architecture

As shown in Table II, the proposed methodology has achieved higher results on the basis of selected criteria, compared with the previous methodology [16-18]. The proposed model has 98.15% of sensitivity to classify malignant images, and 98.41% of specificity representing the correct rejection of benign images. Overall accuracy achieved by the proposed methodology is 98.32%, which is higher than the previous attempts [16,17,18]. This may be that the methodology proposed in this study emphasizes on both segmentation and CNN accuracy. The accuracy of whole methodology is based on accurate image segmentation, as a result, an increase of classification accuracy by CNN is obtained.

V. Conclusion

In this paper, the methodology was proposed to detect melanoma cancer using CNN architecture. Dataset acquired from ISBI2016 was divided into two categories (melanoma and non-melanoma images). Custom-created automated segmentation was applied for this specific problem, also new approach was devised for the implementation of the CNN methodology. CNN was used to extract the image features and ANN was also used to classify those extracted features. ANN consisted for three-fully-connected layers. Results acquired from the proposed methodology yielded a sensitivity of 98.15%, specificity of 98.41%, and accuracy of 98.32%. These figures showed improved results as compared to previous methodologies. Higher results in this study were noninheritable as the proposed methodology emphasizes on each segmentation and CNN accuracy. Accuracy of whole methodology is based totally on accurate segmentation of image. This results in high classification accuracy. The Lesion Feature Network was proposed to address the task of dermoscopic feature extraction and is a CNN-based framework trained by the patches extracted from the dermoscopic images. To the best of our knowledge, we are not aware of any previous work available for this task. Hence, this work may become a benchmark for subsequent related research.

VI . References

1. A. F. Jerant, J. T. Johnson, C. Demastes Sheridan, and T. J. Caffrey, “Early detection and treatment of skin cancer.” American family physician, vol. 62, no. 2, 2000.
2. H. W. Rogers, M. A. Weinstock, S. R. Feldman, and B. M. Coldiron, “Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in us population, 2012,” JAMA Dermatology, vol. 151, no. 10 , pp. 1081–1086, 2015.
3. R. L. Siegel, K. D. Miller, S. A. Fedewa, D. J. Ahnen, R. G. Meester, A. Barzi, and A. Jemal, “Colorectal cancer statistics, 2017,” CA: a cancer journal for clinicians, vol. 67, no. 3, pp. 177–193, 2017.
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Analysis towards Melanoma

Abstract— Melanoma is only harmful in human epidemic diseases and the level of these diseases is increasing continuously. Computers are not more intelligent than humans, but it is easy to find some information easily, which may not be easily apparent to the human eye. Such as: skin color variations or taxa variations. As knowledge is in inadequate source, automated systems proficient of detecting disease could save lives, reduce unnecessary biopsies, and reduce costs. My research is based on the melanoma computer aspect based on: taking pictures of harmful skin though it is very difficult to acquire a large amount of medical image data required for deep learning and to learn if there are many medical image data but they are not properly labeled. In our work we used, the International Skin Imaging Collaboration 2018(ISIC) dataset, is a challenge focusing on the automatic analysis of skin lesions. In our paper, we propose three deep learning methods and a system that combines current developments in deep learning with well-known machine learning approaches. The goal of my research is to use artificial intelligence to make an actual and automated diagnosis method that are capable of segmenting skin lesions, as well as examining the detected part and nearby tissue for melanoma detection.

Keywords— Skin Lesion, Melanoma, CNN, VGG-16, Random Forest.

I. Introduction

Skin is the main part of the human body, which aids to cover the muscles, bones beside the whole body. Skin is uncovered to the outer environment; thus, the disease and infection occur more to the skin. So, proper attention to skin disease is essential. Nowadays lot of people are suffering from skin diseases. It is one of the most common diseases in humans and its frequency is increasing noticeably. Melanoma is the deadliest form of skin cancer. Though melanoma accounts for only 4% of all skin cancers, it is responsible for 75% of all skin cancer deaths [1]. If the symptoms are identified, this disease can be treated at its early stage and can be cured; but if it is identified too late, it can grow deeper into the skin, speared to other parts of the body and can be dangerous, as it becomes difficult to treat. One out of five Americans will be suffering from skin cancer by the age of 70. There are different types of skin diseases. In our dataset, we discuss 7 types of skin diseases; these are: Actinic Keratoses and intraepithelial carcinoma/ Bowen’s disease(akiec), basal cell carcinoma (bcc), benign keratoses like lesions (solar lentigines/seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma(df), melanoma(mel), melanocytic nevi(nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc). But skin cancers fall into generally two categories: melanoma skin cancer and non-melanoma skin cancer. The most recognized skin cancers, basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), are non-melanomas and once in a while risky (perilous). Among many applications of image processing, diagnosis of diseases by detecting particular features from medical images is now very significant. The computerized diagnostics is helpful to enhance the diagnostic accuracy as well as the speed. For these reasons, developing algorithms for diagnosis from medical images has become a major area of research in the area of medical science. Among the different of computerized image processing methods used, various image processing methods are classified to low-level visual feature representations, segmentation algorithms and classical machine learning techniques such as k-nearest neighbor (kNN), support vector machines (SVM) [3] and convolutional neural networks (CNNs) [1]. A computer is not more intelligent than humans but it may be able to extract some information, like color variation, asymmetry, texture features, that may not be readily apparent by human eyes. Nevertheless, automatic recognition of melanoma from dermoscopy images is still a difficult task, as it has several challenges.

This research attempts to relate ideas of Machine Learning and Computer Vision to answer the problem of detection and classification of Melanoma skin cancer. The main objective of this work is to classify cancer from the images by detecting the characteristics present in the affected cancer lesion. Various machine learning techniques, such as artificial neural network and its different variants, unsupervised learning method like K-means clustering, support vector machine, Can give promising results in the detection and classification Task; for example, k-means clustering clusters the infected and non-infected region in the different clusters and hence, the skin cancer can be successfully categorized. Deep neural network has been found to be very efficient among various types of machine learning techniques. Due to the non-linear behavior of the technique, it can be effectively applied to images as well. Convolutional neural network (CNNs) consists of a mass of convolutional modules, and every module usually contains three kinds of layers; convolutional layer, pooling layer and fully connected layer. The key steps in an image processing-based diagnosis of skin diseases are: image acquisition of skin lesion image, segmentation of the skin lesion from a skin region, extraction of features of the lesion spot and feature classification. Here, a method for detecting melanoma using convolutional neural network-based skin lesion analysis has been proposed. In this work, Image processing-based skin lesion analysis provides an effective way to perform classification of cancer and thus, can be a guide in the clinical process of diagnosis.

AI. Related Work

Detecting different types of skin diseases from lesion image is quite challenging. For segmentation of skin lesion in the image, existing methods both use manual, semi-automatic or fully automatic edge detection techniques. Recently a lot of researchers done in this field, hence it’s quite hard to get a better results.

Nurul Huda Firdaus Mohd Azmi et al. [2] presented an analysis of the segmentation method called the ABCD rules (Asymmetry, Border irregularity, Color variegation, Diameter) in image segmentation. The authors showed that the rule effectively classifies the images with a high value of total dermatoscopy score (TDS). The research was carried on malignant tumor and benign skin lesion images.

A technical survey by O. Abuzaghleh et al. [5] proposed two major models. The first model is a real-time alert to help users to check skin injury caused by sunlight. The second model is an image analysis module, which have image input, hair detection, and removal, lesion segmentation, feature extraction and classification. The proposed method used PH2 dermoscopy image database from Pedro Hispano Hospital in Portugal. The database has 200 dermoscopy images of lesions, with benign, atypical and melanoma cases. The experimental results showed that the proposed method is effective, classifying of the benign, atypical, and melanoma images with an accuracy of 96.3%, 95.7% and 97.5% individually.

Teck Yan Tan et al. [11] employed pre-processing such as dull razors and median filters to remove hair and other noises. Segmentation is done by using the pixel limitation technique to separate lesions from the image background. Support Vector Machine (SVM) classifier performs benign and malignant lesion recognition. The method was evaluated using the Dermofit dermoscopy image database with 1300 images and achieved an average accuracy of 92% and 84% for benign and malignant skin lesion classification. Genetic Algorithm (GA) is also applied to identify the most discriminative feature subsets to improve classification accuracy.

M.H. Jafari et al. [12] proposed methods for skin lesion segmentation in medical images using deep learning technique. Then pre-processing the input image, segment the image, shows the lesion region and using deep convolutional neural network (CNN) for skin lesion classification analysis. The result is calculated using Dermquest database. Also, Yading Yuan et al. [16] proposed 19-layer deep convolutional neural networks (CNNs) that are designed a unique loss function. The method achieved promising segmentation accuracy and was calculated using two freely accessible databases ISBI 2016 challenge dataset and PH2 database.

Codella et al. [1] in their paper proposed a method for the identification of melanoma in dermoscopy images by combining deep learning, sparse coding and support vector machine (SVM) learning algorithms. They claimed that their method is helpful since it used unsupervised learning, aiming at avoiding lesion segmentation and complex pre-processing. Kawahara et al. [4]. used a fully-convolutional neural network from skin lesions in order to categorize melanomas with higher accuracy (Kawahara et al., 2016). Later, Codella et al. [1] recommended a method that combines deep learning methods that can segment skin images for analysis. This study trains a model to identify melanoma-positive dermoscopy images running a CNN model, the aim of melanoma recognition with higher accuracy.

BI. Background Theory

A. Convolutional Neural Networks

Convolutional neural networks (CNN) are a type of the neural network that is particularly suited for image analysis. Convolutional neural networks are widely used for image classification, recognition, objects detection [13]. A typical CNN architecture contains convolutional, pooling and fully-connected layers. Relatively novel techniques such as batch normalization, dropout and shortcut connections [14] can additionally be used to increase classification accuracy.

B. Conv Net Architecture

For Convolutional Neural Networks, VGGNet is a well-documented and generally used architecture. It is now very popular due to its impressive performance on image data. For best-performing (with 16 and 19 weight layers) have been made publicly available. In this work, the VGG16 architecture was chosen, since it has been found easy to be applied on different types of datasets. Make things easier well to other datasets. During training, the input to our ConvNets is a fixed-size 224 X 224 RGB image. The only pre-processing, we do is subtracting the mean RGB value, computed on the training set, from each pixel. The image is passed through a stack of convolutional layers, where we use filters with a very small receptive field 3 X 3 (which is the smallest size to capture the notion of left/ right, up/down, center). Only max pooling is used in VGG-16. The pooling kernel size is always 2*2 and the stride is always 2 in VGG-16. Fully connected layers are implemented using convolution in VGG-16. Its size is shown in the format n1*n2, where n1 is the size of the input tensor, and n2 is the size of the output tensor. Dropout is a technique to improve the generalization of deep learning methods. It sets the weights connected to a certain percentage of nodes in the network to 0 (and VGG-16 set the percentage to 0.5 in the two dropout layers) [15]. The input layer of the network expects a 224×224 pixel RGB image. All hidden layers are set with a ReLU (Rectified Linear Unit) as the activation function layer (nonlinearity operation) and include three-dimensional pooling through use of a max-pooling layer. The network is established with a classifier block consisting of three Fully Connected (FC) layers.

Fig. 1. VGG-16 Architecture

C. Support Vector Machine

Support vector machines (SVMs, also support vector networks) is a supervised learning model that studies data used for classification and regression analysis. An SVM model is a symbol of the examples as points in space, mapped so that the examples of the detached groups are separated by a clear gap that is as wide as possible. New examples are then mapped into that same space and expected to belong to a group based on which side of the gap they fall. In addition, the task of linear classification, SVMs can perform non-linear classification using the kernel trick, indirectly mapping their inputs into high-dimensional feature spaces.

D. Random Forest

Random Forest is a supervised learning algorithm. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Random Forest is a flexible, easy-to-use machine learning algorithm that produces, even without hyper-parameter modification, a great result most of the time. It is also one of the commonly used algorithms, due to its simplicity and the fact that it can be used for both classification and regression tasks. The forest it builds, is an ensemble of decision Trees, most of the time trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result. Random Forest has nearly the same hyperparameters as a decision tree or a bagging classifier. Random Forest adds additional randomness to the model while growing the trees [7]. Instead of searching for the most important feature while splitting a node, it searches for the best feature among a random subset of features. These results in a wide range that generally results in a better model. Therefore, in a random forest, only a random subset of the features is taken into consideration by the algorithm for splitting a node. Random forest is a collection of decision trees, but there are some differences. One difference is that, deep decision trees might suffer from overfitting. Random forest prevents over-fitting most of the time, by creating random subsets of the features and building smaller trees using these subsets. Random forests are a way of averaging multiple deep decision trees, trained on different parts of the same training set, with the goal of reducing the variance.

IV. Proposed Methodology

In this research, several methods, from classical machine learning algorithms like SVM, tree-based algorithm Random Forest, and deep learning-based algorithm have been investigated. The process of disease detection and classification is shown in the below figure.

A. Pre-Processing

In our work, we attempt to keep the pre-processing steps, minimal to confirm better generalization skill when tested on other dermoscopic skin lesion datasets. We thus only apply some standard pre-processing steps. First, we normalize the pixel values of the images. Next, the images are resized and the size of 224 x 224 pixels.

B. Data Augmentation

Data Augmentation is a method that is used to avoid overfitting when training Machine Learning models. The goal of data augmentation is to learn how to raise our data set size to train robust Convolutional Network models with limited or small amounts of data. This study is required for improving the performance of an image classification model. Some of the simplest augmentations that are flipping, translations, rotation, scaling, color enhancement, isolating individual R, G, B color channels, and adding noise, etc. Generating augmented input for CNN, using image analysis filters the traditional input to CNN architecture consists of whole images, or image patches, of a standard size, in RGB format. In this work, we augment the input of the CNN with the response of a number of well-established filters that are frequently used for image feature extraction. We augment the training set by blurring the images and we use Gaussian blur for reducing noise and making the image smoother. After that we convert the RGB image to enhance the red color on the image and apply a detached layer on an image, later a partition is performed on those images. This augmentation leads to an increase of training data.

Fig. 3. Data Augmentation

Fig. 2. Flow Diagram of our model

The results of this research have the potential to be used as a practical tool for diagnosis.

C. Image Segmentation

Image segmentation is an important area in an image processing background. It is the process to classify an image into different groups. There are many different methods, and k-means is one of the most popular methods. K-means clustering in such a fashion that the different regions of the image are marked with different colors and if possible, boundaries are created separating different regions. The motivation behind image segmentation using k-means is that we try to assign labels to each pixel based on the RGB values. Color Quantization is the process of reducing the number of colors in an image. Sometimes, some devices may have a constraint such that it can produce only a limited number of colors. In those cases, also color quantization is performed. Here we use k-means clustering for color

quantization. There are 3 features, say, R, G, B. So, we need to reshape the image to an array of Mx3 size (M is the number of pixels in the image). We also set a criteria value for k-means which defines the termination criteria for k-means. We return the segmented output and also the labeled result. The labeled result contains the labels 0 to N, where N is the number of partitions we choose. Each label corresponds to one partition. And after the clustering, we apply centroid values (it is also R, G, B) to all pixels, such that resulting image will have a specified number of colors. And again, we need to reshape it back to the shape of the original image.

D. Classification

For disease classification, we use a collection of recent machine learning models such as SVM, Random Forest, Convolutional neural networks. While implementing deep learning algorithms, I have chosen one novel Convolutional neural network architecture and it is the VGG-16 model. In our system, we propose to use the method of segmentation, classification and Convolution Neural Network. Since we have only a little amount of data to feed into Convolutional neural networks, we used data augmentation to increase the size of our training data, so that it fits well on the validation data. This classification method proves to be efficient for most of skin images.

Result and Discussion

E. Datasets and challenges

There are relatively few data sets in the field of dermatology and even fewer datasets of skin lesion images. Moreover, most of these datasets are too small and/or not publicly available, which provides an additional obstacle to performing reproducible research in the area. Examples of dermatology-related image datasets used in recent research include: The dataset for workshop ‘ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection’ (ISIC 2018) is used [6], [7]. In the training set, there are a total of 10015 skin lesion images from seven skin diseases- Melanoma (1113), Melanocytic nevus (6705), Basal cell carcinoma (514), Actinic keratosis (327), Benign keratosis (1099), Dermatofibroma (115) and Vascular (142). The validation dataset consists of 193 images. The final dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes.

F. Evaluation Metrics

The model evaluation is performed using the same training and testing partition used in the ISIC dataset. CNN architectures with lower loss and greater accuracy were considered in creating a novel CNN model. For quantitative evaluation of the performance their have three commonly used metrics; Recall or Sensitivity (SENS), Classification accuracy (ACC), and Specificity (SPEC) and in our work we were used accuracy performance. These are defined in terms of the numbers of true positives (TPs), true negatives (TNs), false negatives (FNs), and false positives (FPs) [5]. The metrics are defined as follow:

The number of correct predictions divided by the total number of predictions. Accuracy calculates the proportion of predicted pixels that are correctly identified in the predicted binary image.

Accuracy=

Sensitivity or recall evaluates the ability to find the pixels which contain skin lesion in the binary image.

Recall =

Precision, the fraction of recovered instances that are related. It is also alike to positive analytical value.

Precision =

TABLE 1.PRECISION AND RECALL RESULTS ON OUR SVM, VGG16 &

RANDOM FOREST MODEL

Model

SVM

VGG16

Random Forest

Name

Precision

Recall

Precision

Recall

Precision

Recall

NV

1

1

0.64912

1

0.97368

1

2

MEL

0.973684

1

1

0.7027

0.92857

0.702702

BKL

1

1

0.90476

1

0.98039

1

1

BCC

1

1

0.98039

1

0.925

1

2

AKIEC

1

1

0.925

1

0.97674

1

VASC

1

1

0.97674

0.9074

0.92452

0.907407

4

DF

1

0.96969

0.92452

0.9090

0.88235

0.909090

8

9

MEAN

0.99624

0.99567

0.90864

0.9313

0.94161

0.931314

1

Accuracy results for each model are shown in Table 2, respectively. Best values are highlighted (in bold).

TABLE 2. MODEL EVALUATION

Model

SVM

VGG16

Random

Name

Forest

Accuracy

0.903708987

0.9153 ± 0.034

0.897318007

V. Conclusion And Future Scope

The aim of our work is dermoscopic images analysis tools to enable the automated diagnosis of melanoma from dermoscopic images, we can easily detect the disease and can take proper steps to save our times. Image analysis of skin lesions contains three stages: segmentation, feature extraction and disease classification. The different methods

were proposed for each stages of lesion analysis in the papers. We Used Support Vector Machine, VGG-16 and Random Forest. We got 0.903 in Support Vector Machine, 0.9153 ± 0.0343 in VGG-16 and 0.8973 in Random Forest. In assumption, there should be normal actions and freely existing datasets for the new researchers so that together we can fight against this deadliest disease. In future work, on one hand, we will collect more training data to cover the large variation of data distribution, on the other hand we will investigate other techniques.

References

1. Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A., & Smith, J. R. (2015, October). Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. In International Workshop on Machine Learning in Medical Imaging (pp. 118-126). Springer, Cham. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
2. Nurulhuda Firdaus Mohd Azmi, Haslina Md Sarkan, Yazriwati Yahya, and Suriayati Chuprat. Abcd rules segmentation on malignant tumor and benign skin lesion images. In Computer and Information Sciences (ICCOINS), 2016 3rd International Conference on, pages 66–70. IEEE, 2016.
3. Yuan, X., Yang, Z., Zouridakis, G., & Mullani, N. (2006, August). SVM-based texture classification and application to early melanoma detection. In Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE (pp. 4775-4778). IEEE.
4. Kawahara, J., BenTaieb, A., & Hamarneh, G. (2016, April). Deep features to classify skin lesions. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 1397-1400). IEEE.
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6. https://challenge2018.isic-archive.com/task3/
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Introduction:

Melanoma and non-melanoma skin cancer are the most common type of malignancy in the Caucasian population (1-10). The incidence of both MM and NMSC is on the rise ,with an annual increase in MM of 0.6% among adults over 50 years (11). The incidence of melanoma may be even higher than indicated, as the national cancer registries has reported an underestimation of its incidence in certain countries. Consistent epidemiologic and experimental studies have demonstrated that UV-emitting tanning devices cause melanoma and non-melanoma skin cancer. Non-melanoma skin cancer occurs in all races worldwide, and the most important factor related to the development of these malignancies is skin type. However, other risk factors include UV-B exposure(2), chemical exposure, tanning bed use, human papillomavirus (HPV) infection, immunosuppression, and others. In individuals with fair skin, approximately 75% to 80% of non-melanoma skin cancers are basal cell carcinomas, and up to 25% are squamous cell carcinomas. Heritable defects in DNA repair mechanisms, as seen in xeroderma pigmentosum and Muir-Torre Syndrome, also make afflicted individuals at high risk for cutaneous ‘carcinomas. In India, skin cancers constitute about 1-2% of all the diagnosed cancer (4).

Methodology

The current data was extracted from highly reputed journals on PMC, Pubmed, Scopus Comment by MAZHAR: Eg total 230 articles and skin cancer, keywords were used, specific Comment by MAZHAR: Science direct, Wiley online

1.1 Pathophysiology

(Angiogenesis Increased VEGF AND iNOS) Intermittent, recreational sun exposure, more so than cumulative UV radiation is a significant risk factor for the development of basal cell carcinoma. The gene most often altered in basal cell carcinoma is the PTCH gene and the second most common alteration is point mutations in the p53 gene. Unlike the complex relationship between basal cell carcinoma and UV radiation, squamous cell carcinoma is related to the cumulative lifetime dose of UV radiation. Current opinion favors clonal expansion of keratinocytes with a p53 mutation causing precancerous actinic keratosis lesions with slight dysplasia which precede further severe dysplasia and transformation into invasive squamous cell carcinoma.

Molecular pathophysiology

(Inflammation Increased COX-2, PCNA, PGs, MPO Increased CPX 2 and Pgs, Mpo,)

(Immunosuppression Increased IL 10,IL 6 and 12 decreased)

(Skin cancer)

(Skin carcinogen Increased DNA adduct, ros, lipid peroxidation)

(Unregulated proliferation)

(apoptosis)

(DNA damage NER decreased and chromosomal aberration increased)

Extrinsic pathway intrinsic pathway

Inflammation or infection oncogenic activation

(Transcription factors (NF-κβ, STAT3) activated in tumor cells)

(Chemokines, cytokines, prostaglandins, and COX-2 produced by tumor cells)

(Inflammatory cells recruited)

Mast cell eosinophil neutrophil (Transcription factors (NF-κβ, STAT3) activated in tumor cells, stromal

cells and inflammatory cells, stromal cells, and inflammatory ells)

(Chemokines, cytokines, prostaglandins and COX-2 produced)

Current status

Treatment of precancerous lesions and cutaneous carcinoma should be tailored toward the individual patient scenario and the best clinical outcome. If presenting as isolated lesions, precancerous actinic keratoses can be treated individually with lesion-directed therapies such as cryotherapy. Often patients may present with numerous lesions and diffuse actinic damage which require field-directed therapy as opposed to individually treating each lesion. This can be done with topical agents (5-fluorouracil, imiquimod, and ingenol mebutate) or with photodynamic therapy after sensitizing the skin with a topical agent. Initial pre-emptive efforts should be made to reduce the patient’s risk profile for developing cutaneous carcinoma including optimizing the immunosuppressant regimen in solid organ transplant patients, proper surveillance schedules in patients treated with immunomodulatory therapies, and adequate therapy of precancerous lesions.

Basal cell carcinomas and squamous cell carcinomas, if superficial, can be treated with topical therapies depending on provider preference. However, the standard practice is to surgically treat these lesions with destructive means such as electrodesiccation and curettage or surgical excision. Skin cancers greater than 2 cm in diameter and those located on functionally and cosmetically sensitive sites (head/neck, hands, and feet, genitalia) usually are referred for a special surgical procedure called Mohs micrographic surgery. Some patients with aggressive and recurrent forms of basal cell carcinoma who would not be good surgical candidates are treated with radiation therapy or a systemic medication called Vismodegib which inhibits cellular proliferation.

Melanomas are the most aggressive form of skin cancer; the gold standard of treatment is surgical excision. If caught early, surgical excision can be curative. Later stage tumors portend a poor prognosis and often require adjuvant immunotherapy.

Models

The skin is the largest organ of the body, made up of multiple layers.

Current mouse models of BCC involve activation of some component of the Hh signal transduction pathway. (2015)

The canonical carcinogenesis model that cSCC development is a multistep progression starting from the precancerous actinic keratosis (AK) in which keratinocyte atypia is confined to only PORTION OF THE Epidermis leading to abnormal differentiation and stratum corneum thickening with retained nuclei. Mutation in p53, an important tumor suppressor whose inactivation has been implicated in a variety of tumors, has been implicated in a variety of tumors, identified in UV exposed skin as well as the majority of AK/SCC.

K14-SCF; XPA Mouse model (K 14 promoter stem cell factor xeroderma pigmentosa A complement group): the k-14 –SCF transgenic mice do not spontaneously form melanoma however when expressed in mice lacking the XPA they formed metastatic melanocytic skin tumors after UV exposure in about 30% of animals.

Melanoma animal models are enhanced with regard to tumor penetrance and latency by exposing the mice to additional mutagens such as UV light or DMBA/TPA.

K14-Fyn : Fyn is a potent oncogene in the skin. K-14 transgene develops precancerous lesions and invasive squamous cell carcinomas (SCCs) spontaneously in 5 to 8 weeks.

Patched knockout mouse models of basal cell carcinoma (PTCH) :-PTCH knockout mouse models to investigate BCC as well as for potential use in preclinical research.

Delineating molecular mechanisms of squamous tissue homeostasis and neoplasia: focus on p63: P63, a transcription factor that plays an essential role in the development and maintenance of normal stratified squamous epithelium.

Role of stat3 in skin carcinogenesis: insights gained from relevant mouse models”

Models of skin cancer

Name of model

Name of cell line/mechanism

Dose/medium

No of days for induction

Breed

Melanoma models

(a) models that facilitate the study of melanoma progression and its underlying mechanisms using preexisting malignant cells and (b) allow generation of melanomas through manipulation of specific genetic event

Oncogene induced DNA damage model

Activating ATM/TP53/MDM2 checkpoint pathway

DMBA/TPA induced (Rajmani et al;2011)

AgNOR and PCNA staining

Mutation in hras gene and trp 53

1%

20 to 25 weeks

Wistar rats

UV induced skin cancer (UV B)

TP53 mutation

RAS mutation

transgenic mice used in UV radiation-induced skin cancer studies was a strain of mouse which carried the SV40 T antigen under control of the tyrosine promoter and expressed only in melanocytes

In vivo models of skin cancer

gene

Mouse model

Phenotype

reference

Akt

Akt1 knockout

In the DMBA/TPA model, Akt12/2 mice develop tumors with reduced yield and size

Skeen et al. 2006

Cyclin D1

Cyclin D1 knockout

Cyclin D12/2 mice develop papillomas with increased latency and reduced incidence and yield in the DMBA/TPA mode

Robles et al. 1998

Erk

Erk1 knockout

Erk12/2 mice show reduced skin inflammation and proliferation in response to TPA treatment and are tumor-resistant in the DMBA/TPA model

Bourcier et al. 2006

Fos

c-fos knockout

c-fos-deficient papillomas quickly become dry and hyperkeratinized, and fail to progress to malignancy

Saez et al. 1995

Jnk

Jnk1 and Jnk2 knockouts

In the DMBA/TPA model, Jnk12/2 mice show enhanced tumor susceptibility while Jnk22/2 mice are tumor resistance

Chen et al. 2001; She et al. 2002

Jun

c-jun knockout in the epidermis using K5-Cre

In the K5-SOS-F skin tumor model, c-jun ablation leads to smaller papillomas that show increased differentiation, possibly caused by down-regulation of EGFR

Zenz et al. 2003

Mek

Overexpression of Mek1 in basal keratinocytes and hair follicle ORS using the K14 promoter

Mek2 knockout and conditional Mek1 knockout using K14-Cre

Epidermal hyperplasia and spontaneous skin tumor formation.

In the DMBA/TPA model, Mek1 knockout but not Mek2 knockout impedes tumorigenesis; in a mouse model of oncogenic Ras-driven skin cancer; however, both Mek1 and Mek2 (or at least one copy of each) have to be deleted to impede carcinogen

Feith et al. 2005

Scholl et al. 2009a,b

Myc

K5-Myc transgenic mice

K14-driven Myc overexpression

Spontaneous papilloma and SCC development; mice are also more tumor susceptible in the DMBA/TPA model

Epidermal hyperplasia, enlarged sebaceous glands, spontaneous skin lesions and stem cell loss; DMBA/TPA-treated K14-Myc mice develop tumors with reduced latency and increased yield, but these are predominantly sebaceous adenomas

Rounbehler et al. 2001

Arnold and Watt 2001; Waikel et al. 2001; Honeycutt et al.2010

Pak1

Pak1 knockout

Pak1 deficiency impedes tumor development and progression in a mouse model of KrasG12D-driven skin cancer

Chow et al. 2012

PKC

PKC-h knockout; K5-driven PKC-a overexpression; K14-driven PKC-d or PKC-1 overexpression

In the DMBA/TPA model, PKC-h2/2 and K5- PKC-a mice show enhanced tumor formation; K14-PKC-d and K14-PKC-1 mice, on the other hand, are resistant to papilloma development; K14-PKC-1 mice also show increased de novo carcinoma

Reddig et al. 1999, 2000; Chida et al. 2003; Cataisson et al. 2009

Rac1

Keratinocyte-specific deletion using K5- and K14-Cre

Hair follicle (and epidermal) stem cell loss/ impairment; K5-driven Rac1 ablation leads to tumor-resistance in the DMBA/TPA model, associated with a decrease in keratinocyte prolifera

Keely et al. 1997; Benitah et al. 2005; Chrostek et al. 2006; Wang et al. 2010

The K14-Shh transgenic mice developed cutaneous BCC-like tumors within 4 days of embryonic skin development [11]. Similar spontaneous BCC-like tumors were found in mice over-expressing a mutant variant of SMO (SMO-M2) under the control of the K5 promoter [12]

PTCH1 heterozygous mice (PTCH1+/−) spontaneously develop microscopic BCC and after chronic UV exposure, the PTCH1+/− mice develop rapidly growing BCC-like tumors after 4 months [15].

Skin cancer cell lines as per ATCC

Melanoma cell lines

s.no

Cell line

Cell type

Disease

application

1

A-375-P

Melanocyte

Malignant melanoma

This cell line is useful as a control for A375-MA1 and A375-MA2 to study the mechanisms of metastasis. It has been used with microarray analyses to identify metastasis-specific genes using a functional genomics approach and in proteomics analyses

2.

NRAS-mutant-A375 Isogenic-Luc2 (ATCC® CRL-1619IG-2-LUC2™)

melanocyte

malignant melanoma

BRAF drug resistant melanoma model. Excellent signal/background ratio and stable Luciferase expression make this cell line ideal for in vivo bioluminescence imaging of xenograft animal model to study human cancer and monitor activity of anti-cancer drug. It also can be used in cell-based assays for cancer research

3.

VMM15 (ATCC® CRL-3227™)

Melanocyte

Melanoma, Stage IIIC; malignant

Drug screening

Development of targeted therapy

Development of combination therapy

Tumor vaccine development

4.

VMM917 (ATCC® CRL-3232™)

Melanoma

Melanoma, Stage IV; malignant

Drug screening

Development of targeted therapy

Development of combination therapy

Tumor vaccine development

5.

VMM5A (ATCC® CRL-3226™)

Melanocyte

Melanoma, Stage IIIC; malignant

Drug screening

Development of targeted therapy

Development of combination therapy

Tumor vaccine development

6.

VMM39 (ATCC® CRL-3230™)

Melanocyte

Melanoma, Stage IIIC; malignant

Drug screening

Development of targeted therapy

Development of combination therapy

Tumor vaccine development

7.

A375.S2 (ATCC® CRL-1872™)

Melanoma

malignant melanoma

8.

VMM425 (ATCC® CRL-3231™)

Melanocyte

Melanoma, Stage IV; malignant

Drug screening

Development of targeted therapy

Development of combination therapy

Tumor vaccine development

9.

A375-MA2 (ATCC® CRL-3223™)

malignant melanoma

This cell line is useful to study the mechanisms of metastasis. It has been used with microarray analyses to identify metastasis-specific genes using a functional genomics approach and in proteomics analyses.

10.

VMM1 (ATCC® CRL-3225™)

Melanocyte

Melanoma, Stage IV; malignant

Drug screening

Development of targeted therapy

Development of combination therapy

Tumor vaccine development

11.

KRAS mutant-A375 Isogenic-Luc2 (ATCC® CRL-1619IG-1-LUC2™)

melanocyte

malignant melanoma

BRAF drug-resistant melanoma model. Excellent signal/background ratio and stable Luciferase expression make this cell line ideal for in vivo bioluminescence imaging of xenograft animal model to study human cancer and monitor activity of the anti-cancer drug. It also can be used in cell-based assays for cancer research.

12.

VMM18 (ATCC® CRL-3229™)

Melanocyte

Melanoma, Stage IIIB; malignant

Drug screening

Development of targeted therapy

Development of combination therapy

Tumor vaccine development

13.

SKIN CANCER CELL LINES

s.no

Cell line

Cell type

disease

Application

1

182-PF SK (ATCC® CRL-1532™)

hereditary adenomatosis

2

A-375 [A375] (ATCC® CRL-1619™)

malignant melanoma

This cell line is a suitable transfection host. This cell line is also the ideal control for NRAS mutant-A375 isogenic cell line (ATCC®CRL-1619IG-2™).

3.

A-431 (ATCC® CRL-1555™)

epidermoid carcinoma

This cell line is a suitable transfection host.

4.

A.P. (ATCC® CRL-6295™)

normal

This cell line is neither produced nor fully characterized by ATCC. We do not guarantee that it will maintain a specific morphology, purity, or any other property upon passage.

5.

A2058 (ATCC® CRL-11147™)

melanoma

This cell line is a suitable transfection host

6.

A375-Luc2 (ATCC® CRL-1619-LUC2™)

malignant melanoma

Excellent signal/background ratio and stable luciferase expression make this cell line ideal for in vivo bioluminescence imaging of xenograft animal model to study human melanoma and monitor activity of anti-cancer drug. It also can be used in cell-based assays for cancer research.

7.

A375-MA1 (ATCC® CRL-3222™)

malignant melanoma

This cell line is useful to study the mechanisms of metastasis. It has been used with microarray analyses to identify metastasis-specific genes using a functional genomics approach and in proteomics analyses.

8

A375-MA2 (ATCC® CRL-3223™)

malignant melanoma

This cell line is useful to study the mechanisms of metastasis. It has been used with microarray analyses to identify metastasis-specific genes using a functional genomics approach and in proteomics analyses.

9

A7 [M2A7] (ATCC® CRL-2500™)

melanoma

melanoma

A7 [M2A7] cells are useful as a control for studying filamin systems (cell signal transduction, cell membrane sorting and cytoskeleton-membrane association).

10

Ad Hot (ATCC® CRL-1227™)

Ehlers-Danlos syndrome, type II

11

Am Coo (ATCC® CRL-1286™)

osteogenesis imperfecta (tarda)

12

Amdur II (ATCC® CCL-124™)

Fibroblast

methylmalonic acidemia

13

An Zan (ATCC® CRL-1266™)

Marfan syndrome

14

Ar Ke-2 (ATCC® CRL-1324™)

Ehlers-Danlos syndrome, presumed heterozygote

15

B16-F0 (ATCC® CRL-6322™)

melanoma

This cell line is a suitable transfection host.

16

B16-F1 (ATCC® CRL-6323™)

melanoma

This cell line is a suitable transfection host.

17

B16-F10 (ATCC® CRL-6475™)

melanoma

This line is a suitable transfection host.

18

Ba Pot (ATCC® CRL-1280™)

osteogenesis imperfecta (congenita)

19

Be Ar (ATCC® CRL-1167™)

xeroderma pigmentosum, presumed heterozygote

20

Bi Fin (ATCC® CRL-1219™)

Ehlers-Danlos syndrome

21

BJ (ATCC® CRL-2522™)

Fibroblast

normal

The cells may be used for stable transfection studies.

22

Bo Gin (ATCC® CRL-1180™)

Ehlers-Danlos syndrome, type I (autosomal dominant type)

23

BUD-8 (ATCC® CRL-1554™)

fibroblast

normal

This line is highly sensitive to interferon and can be used in assays of interferon activity.

24

C 211 (ATCC® CCL-123™)

fibroblast

Cri du Chat syndrome

25

C32 (ATCC® CRL-1585™)

melanoma, amelanotic

26

C32 purified DNA, [3 µg] (ATCC® CRL-1585D™)

he C32 cell line is a human skin malignant melanoma cell line

27

C32TG [C32-r16TG] (ATCC® CRL-1579™)

amelanotic melanoma

28

Caki-1 (ATCC® HTB-46™)

clear cell carcinoma

This cell line is a suitable transfection host.

29

CCD 1102 KERTr (ATCC® CRL-2310™)

keratinocyte; human papillomavirus 16 (HPV-16) E6/E7 transform

6E7 sequences were detected by PCR in cells at passage 18. Major Histocompatibility Complex class I or II molecules were not expressed on these cells, but PCR analyses revealed the presence of the genes for directing the synthesis of HLA antigens.

Major Histocompatibility Complex class I or II molecules were not expressed on these cells, but PCR analyses revealed the presence of the genes for directing the synthesis of HLA antigens.

30

CCD 1106 KERTr (ATCC® CRL-2309™)

keratinocyte; human papillomavirus 16 (HPV-16) E6/E7 transformed

31

CCD-1058Sk (ATCC® CRL-2071™)

fibroblast

normal

The future: the Grand Challenges in Global Skin Health initiative

How then do we achieve these goals? There are four key measures to achieve success: (i) research, (ii) education, (iii) clinical application through translation, and (iv) the support of those responsible for the management and delivery of health care at local and national levels. Financial support for basic and translational research is fragmentary on the global stage. While it is true that not all skin diseases are lethal, they contribute significantly to the global disease burden, and the persisting failure to address their management accounts for a huge loss of production and increased medical expenditure. In Europe, the annual cost of occupational dermatitis – including the direct costs of treatment and industrial compensation, as well as the indirect costs of sick leave and loss of productivity – is estimated to be greater than €5 billion.20 In moderate-to-severe psoriasis the annual cost of the disease in the U.S.A., including treatment and loss of productivity, was estimated to be $1125 billion.21 At the other end of the spectrum the cost of inappropriate and ineffective scabies treatment over a 3-month period in resource-poor settings is enough to eliminate household cash reserves.22 Increasing the availability of cost-effective measures would have a major impact on both personal and institutional economies. Education of frontline health workers in the elements of skin disease is also key to successful management. However, competing priorities in medical and nursing training have squeezed the opportunity to address this issue. A new drive to integrate the core skills and knowledge needed to ensure freedom from skin problems into undergraduate and postgraduate teaching for health professionals could provide the answer. Innovation in healthcare delivery has frequently ignored the needs of the whole patient and the populations in which they live. Skin disease has been a casualty in this respect. However, perhaps the most powerful contribution to making skin health a realizable objective over the next 25 years is the recognition, among leaders of governments and non-governmental organizations, that it is a realistic, affordable and achievable goal, integral to future health and research strategies. The recent actions by the World Health Organization in framing a resolution to member states for concerted action on psoriasis23 and in recognizing scabies as a neglected disease24 provide a huge impetus for change. The international dermatological community is committed to this goal and is resolved to see the adoption of these strategies at all levels of research, education and health care. With this objective in mind the International League of Dermatological Societies has embarked on a focused program, the Grand Challenges in Global Skin Health, to tackle these issues. This has started with initiatives in data collection and analysis, aging research and the formation of international alliances in scabies control and the care of those with albinism. Other schemes will follow over the next few years as members of the international dermatological community respond to these challenges that face our patients.

Treatment Challenges

Treatment strategies for skin cancers require careful consideration, and there are many challenges to overcome. However, with increasing treatment choices, in terms of both therapy combinations and sequences, we can achieve better outcomes for patients with fewer recurrences and longer treatment-free periods

References

1. Whiteman DC, Green AC, Olsen CM. The growing burden of invasive melanoma: projections of incidence rates and numbers of new cases in six susceptible populations through 2031. J Invest Dermatol. 2016;136:1161–1171. doi: 10.1016/j.jid.2016.01.035. [PubMed] [CrossRef]
2. De Vries E, Coebergh JW. Cutaneous malignant melanoma in Europe. Eur J Cancer. 2004;40:2355–2366. doi: 10.1016/j.ejca.2004.06.003. [PubMed] [CrossRef]
3. Lasithiotakis K, Kruger-Krasagakis S, Manousaki A, Ioannidou D, Panagiotides I, Tosca A. The incidence of cutaneous melanoma on Crete, Greece. Int J Dermatol. 2006;45:397–401. doi: 10.1111/j.1365-4632.2006.02492.x. [PubMed] [CrossRef]
4. Mansson-Brahme E, Johansson H, Larsson O, Rutqvist LE, Ringborg U. Trends in incidence of cutaneous malignant melanoma in a Swedish population 1976–1994. Acta Oncol. 2002;41:138–146. doi: 10.1080/028418602753669508. [PubMed] [CrossRef]
5. Stang A, Pukkala E, Sankila R, Soderman B, Hakulinen T. Time trend analysis of the skin melanoma incidence of Finland from 1953 through 2003 including 16,414 cases. Int J Cancer. 2006;119:380–384. doi: 10.1002/ijc.21836. [PubMed] [CrossRef]
6. Ulmer MJ, Tonita JM, Hull PR. Trends in invasive cutaneous melanoma in Saskatchewan 1970-1999. J Cutan Med Surg. 2003;7:433–442. doi: 10.1007/s10227-003-0159-0. [PubMed] [CrossRef]
7. Dennis LK. Analysis of the melanoma epidemic, both apparent and real: data from the 1973 through 1994 surveillance, epidemiology, and end results program registry. Arch Dermatol. 1999;135:275–280.[PubMed]
8. Geller AC, Miller DR, Annas GD, Demierre MF, Gilchrest BA, Koh HK. Melanoma incidence and mortality among US whites, 1969–1999. JAMA. 2002;288:1719–1720. doi: 10.1001/jama.288.14.1719.[PubMed] [CrossRef]
9. Hall HI, Miller DR, Rogers JD, Bewerse B. Update on the incidence and mortality from melanoma in the United States. J Am Acad Dermatol. 1999;40:35–42. doi: 10.1016/S0190-9622(99)70562-1. [PubMed] [CrossRef]
10. Perera E, Gnaneswaran N, Staines C, Win AK, Sinclair R. Incidence and prevalence of non-melanoma skin cancer in Australia: a systematic review. Australas J Dermatol. 2015;56:258–267. doi: 10.1111/ajd.12282. [PubMed] [CrossRef]
11. American Cancer Society. Cancer Facts & Figures 2016. http://www.cancer.org/acs/groups/content/@research/documents/document/acspc-047079.pdf. Accessed 2 Aug 2016.

Before delving into the content of this essay, it is crucial to understand why this argument is valid. Cancer is a fatal disease and the chances of survival, especially for skin cancer, vary greatly depending on how early and accurately it is diagnosed (The Skin Cancer Foundation, 2019). Recognising cancerous cells from benign ones requires not only medical knowledge but precision and intelligence. Would a machine be able to diagnose a patient with skin cancer?

When arguing whether Artificial Intelligence (AI) should, or should not, be used in the diagnosis of skin cancer; it is vital to compare machine diagnosis against a pathologist with a microscope in order to see which is, essentially better. The categories of comparison are as follows: accuracy of diagnosis; empathy for the patient; feasibility and accessibility (Gordon A, 2018).

Artificial Intelligence is dominating the world hence making this project even more captivating. This essay is informative about how vital early diagnosis in skin cancer is to the survival of the patient. However, the main purpose of this research is to provide an argument for and against the use of apps, machinery and algorithms in skin cancer diagnostics. Questions regarding ethics, patient care, accuracy and more will be addressed in the findings of this research.

What is Artificial Intelligence?

In order to comprehend what Artificial Intelligence means, it is vital to to first define Intelligence. Not only is the meaning of intelligence subjective but it is constantly changing. In the 15th century, they had already perceived it to be a form of ‘superior understanding’ and in 1580 it was frequently used in military circles as ‘secret information from spies’ (Pg 23-42, De Spiegeleire S, Maas M, Sweijs T. 2017). Marcus Hutter and Shane Legg, in 2007, conducted a survey of over 70 definitions of intelligence, one of them being ‘ to collect information and form an impression thus leading one to finally understand’ (Smith C, 2006). In conclusion to this study, it was discovered that it is difficult to narrow down to one definition but the general idea conveyed in all of them is the same. The functional definition devised by Stanford University’s Formal Reasoning Group (2017) covers both natural and artificial forms of intelligence,‘ Intelligence is the computational part of the ability to achieve goals in the world’. The purpose of this ability known as intelligence is to, as stated above, achieve goals. Intelligence can be used to solve a puzzle or buy a train ticket all the way to analysing samples to diagnose skin cancer.

Now that the definition of Intelligence has been understood and clarified, we will now be able to understand the concept of Artificial Intelligence. AI is a truly broad subject and has many aspects of it, it can range from complex machinery that can think and comprehend like humans all the way to simple algorithms used to play board games. The term Artificial Intelligence was first coined by John McCarthy in 1956 when he held the first academic conference on this subject. However, the journey to understand if machines can truly think began much before that. In 1950, Alan Turing wrote a paper on the notion of machines being able to simulate human beings and the ability to do intelligent things, such as play Chess (D. L. Dowe, 1998). Technological advancement began decades ago and this shows that it required a lot of research and development to get to where it is today.

In a basic understanding, Artificial Intelligence is the ‘automation of intelligent behavior’ (S. Bringsjord, 2003) but similarly to the term intelligence, it has many deeper layers to its meaning and countless definitions. A commonly quoted definition formulated by the US Defence Science Board upheld in the recent Summer Study on Autonomy is ‘the capability of computer systems to perform tasks that normally require human intelligence’ (2016). However, it is vital to contextualise and use a definition which is most suited to the to healthcare. Artificial intelligence in healthcare is ‘the use of algorithms and software to approximate human cognition in the analysis of complex medical data’, specifically,it is the ability for computer algorithms to approximate conclusions without direct human input (M. S. Ali, 2012). This definition is most appropriate as skin cancer diagnostics requires exactly that, the analysis of complex medical data.

Types of Skin Cancer

In the UK, 37 people are diagnosed with melanoma every day (Macmillan editorial team, 2017). When treated early it can usually be cured, but the disease still claims tens of thousands of lives every year. According to the World Health Organisation (2018), skin cancer accounts for one in every three cancers diagnosed worldwide, proving it is one of the most common cancers. Skin cancer is split into three main categories: basal cell carcinoma; squamous cell carcinoma and melanoma. Basal cell carcinoma (BCC) is a cancer of the basal cells at the bottom of the epidermis. It is occasionally called a rodent ulcer and about 75% of all skin cancers in the UK are BCCs (NHS, 2017). Most BCCs are very slow-growing and almost never spread to other parts of the body. If a patient has a mole which they suspect to be BCC, then it is recommended to get a check up and possibly diagnosis within 18 weeks(), this shows that this cancer is not the most fatal as diagnosis is not of high urgency. Almost all patients with BCC who receive treatment are completely cured.

Squamous cell carcinoma (SCC) is a cancer of the cells in the outer layer of the skin. It is the second most common type of skin cancer in the UK (NHS, 2017). Similarly to BCC, most people treated for SCC are completely cured. Usually, SCCs are slow-growing and they only spread to other parts of the body if they are left untreated for a long time. Occasionally, though, they can behave more aggressively and spread at an earlier stage. Both BCC and SCC are non-melanoma cancers and are, by far, what the majority of skin cancer patients are affected by. However, the most dangerous and urgent of skin cancers falls in the category of malignant melanoma.

Melanoma develops from melanocytes that start to grow and divide more quickly than usual. When they grow out of control, they usually look like a dark spot or an unusual, odd shaped mole on your skin and despite this cancer being more common in lighter complexions, it is not exclusive to them. It is also twice as common in females than males, however more men die from it. In the UK alone, more than 2100 Britons die each year from malignant melanoma (). It is important to find and treat melanoma as early as possible. If a melanoma is not removed, the cells can grow down deeper into the layers of the skin and if the melanoma cells get into the blood or lymphatic vessels, they can travel to other parts of the body. As displayed from the information above, quality and speed of diagnosis plays a large role in a successful recovery hence proving the validity of this essay question. Melanoma’s are the most dangerous type of skin cancer as they root furthest down into the skin. If given time, the melanoma will reach bones and vital tissues which is life-threatening

The Role of AI in the Healthcare industry

As young as AI may be, it is growing rapidly and becoming more and more commonly used. Three centuries ago, the UK faced an industrial revolution (Jacob. B. Madsen, 2010) and today the world has entered a digital revolution. An example of an industry which recently began using technology is transport with the invention of self driving cars such as Tesla and introducing self driving trains like the DLR Service UK. This reduces their costs and improves efficiency as a human is no longer required to carry out this job. Also, the financial industry follows technological advancement with keen interest. Big banks such as JP Morgan have been early adopters of disruptive technologies like Blockchain (C Hudson, 2018). The use of AI in industries is growing rapidly and it has many applications in the industry of Healthcare as well.

https://towardsdatascience.com/ten-applications-of-ai-to-fintech-22d626c2fdac

Madsen, Jakob B., et al. “Four Centuries of British Economic Growth: the Roles of Technology and Population.” Journal of Economic Growth, vol. 15, no. 4, 2010, pp. 263–290. JSTOR, www.jstor.org/stable/40984852.

If there is one industry that reaches everybody in the world, it is the healthcare industry. With the power to save lives, this industry must be on a continual path towards excellence. Living in a world which is becoming more and more digitised with time, it is only natural that many industries will also use technology in order to improve the efficiency of accuracy of their work. The two main branches associated with Artificial Intelligence in medicine are virtual and physical. The virtual component includes machine learning (ML) and algorithms, whereas physical AI includes medical devices and robots for delivering care. AI is used successfully in tumour segmentation, histopathological diagnosis, tracking tumour development, and prognosis prediction (Y Vaishali, 2019). Major disease areas that use AI tools include cancer, neurology and cardiology (Jiang F, 2013). AI, within the last 10 years, has become predominant in the healthcare industry and there must be reasons for it.

Skin cancer diagnosing technology

For many years, skin cancer has been detected visually as this is still the first step towards a diagnosis in skin cancer. Moles are checked for abnormalities and, typically, a biopsy of the skin is taken and examined by specialists under a microscope. The cells are analysed and compared with several other samples before concluding. Cancer diagnosing technology can range from data analysing software in computers, apps, machines and more. This technology being developed for medical use is typically complicated pattern matching: An algorithm is shown many many medical scans of organs with tumors, as well as tumor-free images, and tasked with learning the patterns that differentiate the two categories. The algorithms were shown nearly 200,000 images of malignant, benign, and tumor-free CT scans, both in 3D and 2D. The way that the nodule detection algorithm is measured for accuracy is as it learns to find these tumors is it the same as they would be implemented in a specialist’s office, with a metric called “recall.” Recall tells us the percentage of nodules the algorithm catches, given a set number of false alarms ( D Gershgorn, 2018). The engineering behind this technology requires specialist understand and a lot of research and development especially when being used in diagnosis’ as it is a matter of life and death.

The Figure above displays how Diagnostic Imaging is the most considered medical data. This means, for skin cancer, skin images and biopsies are digitalised, and this is becoming common. The benefit of transferring the image onto a computer is that any specialist across the world, can view that patients sample and analyse it. App 1- Mole Detect Pro

In recent years, as technologies develop, dermatologists and software creators have come together to create apps that the average person can use and check themselves for skin cancer. Despite there being some controversy about skin cancer diagnosing apps, performing self exams is recommended by The Skin Cancer Foundation. Skin cancers that are detected early are almost always prevented from spreading and cured which is why it is important to regularly examine yourself and stay alert to any changes in your skin (Glynn S, 2018). The aim and purpose behind skin cancer detecting apps is to make it easier for the average person to identify whether their mole could be cancerous or not and then make an appointment with a doctor. An example of a skin cancer-detecting app is Mole Detect Pro and it claims to ‘provide its users with a remote professional diagnosis within 24 hours’ using an advanced algorithm to assess the probability of a potential melanoma (Glynn S, 2018). Dr Ashworth said ‘The technology behind this app is pretty impressive’. AI should be used in the diagnosis of skin cancer as it makes the process much more quick and efficient. Another app which has been debated over by many analysts and dermatologists is SkinVision which claims the following: ‘SkinVision helps you check your skin for signs of skin cancer with instant results on your phone. Our clinically-proven technology, combined with the knowledge of dermatologists specialized in skin cancer, helps you keep your skin healthy’ (SkinVison, 2018). The only major difference in the statements made by both Mole Detect Pro and SkinVision is that Mole Detect Pro claims explicitly to provide a diagnosis whereas Skinvision claims that a proper diagnosis can only be done combined with the knowledge of a professional. AI should be used in the diagnosis of skin cancer as the net result of using this app is that a lot more people with a potential problem will end up going to seek a professional diagnosis. This saves costs and time as less people will be visiting the dermatologists with non-cancerous moles.

The reason behind the controversy for allowing apps to diagnose is due to researchers believing there is a lack of rigorous published trials to display that these apps are reliable and therefore safe to use, a lack of input of mole images which are used to create the advanced algorithm that the app is based upon and general flaws in the technology. Dr Mary Martini, an associate professor of dermatology at Northwestern University’s Feinberg School of Medicine commented ‘When an app tells you something is benign when it isn’t, that’s a major problem’. Dr Martini’s concern is logical and shared with a wide community of oncologists. Instead of saving more lives, this has the potential to do the opposite, however, accuracy of AI diagnosis as compared to a doctor is higher therefore this should not be an issue.

Success rate in diagnosis – Machine VS Man

AI machines are programmed to see things far better than a human will ever see. For example it is possible to analyse the retina of a person to detect whether they are male or female, to analyse a colonoscopy and AI will pick up polyps that were missed by doctors (Topol E, 2019). This shows that AI is, in simple terms, smarter than a human. This can also be proved seen in simple calculators as they are able to solve problems faster and with 100% accuracy as compared to a human.

Now, specifically looking into the accuracy of AI in the diagnosis of skin cancer. A study was conducted by the University of Birmingham (2018) where a team from the United States, France and Germany instructed an AI system to distinguish fatal skin lesions from benign ones and in this study, more than 100,00 images were used . In order for this test to be fair, 58 dermatologists where over half of them were at ‘expert’ level with more than 5 years of experience, were also given these images and asked to distinguish between malignant and benign images. On average, dermatologists accurately detected 86.6% of skin cancers from the images presented to them and an AI machine successfully identified 95% of the melanomas (British Association of Dermatologists’, 2018). This shows that the machine was more sensitive and precise while analysing the images then the professionals were. Many dermatologists have been forced to question whether it is true that a machine is able to conduct their job, that they spent years training for, better than they are.

Lack of empathy using AI and its implications – Ai is harming the patient/doctor bond?

Despite the results of this study proving that AI is more accurate in the diagnosis then dermatologists, there are other important aspects of diagnosing skin cancer that need considering, particularly in malignant melanoma skin cancers. This is the quality of care. A key part of a doctor’s role is to be able to console their patients when conveying distressing news to the patient or their families.

Whether a machine is able to provide the empathy that is required while diagnosing skin cancer is not one of much controversy, it cannot unless it is programmed to and even then there are ethical issues arising with allowing machines to turn into comforting robots. The issue arising with allowing robots to, essentially, display emotions is that those emotions are not real. Patient care is regarded of high importance not only within the foundations of the NHS but also the World Health Organisation (WHO). If the use of AI becomes predominant in the field of diagnostics then in order to be in compliance with the 7 Principles of Care (23) the machinery would need to be taught to empathise and communicate in a comforting fashion.

The founder of Google’s empathy Lab, Danielle Krettek said that her work has contributed to some of the Google Assistant’s ability to attune to one’s mood. Danielle Krettek further explained this idea at the design conference Semi Permanent in Sydney, Australia, she said “When you say “Im feeling depressed”, instead of giving you a description of what depression is, it might say “you know what, a lot of people feel that way, you are not alone”. The ability to empathise is a social skill which can be taught to artificial intelligence which then can be practiced among skin cancer patients during diagnosis. A component of affective empathy is when we are able to share the emotions of others. Dr Pascal Molenberghs, a social neuroscientist at the University of Melbourne said “We stimulate in ourselves the emotions we observe in others” and also implied in his speech that if a robot is designed to alter its tone and speech in order to empathise, it may come across as mimicry instead of empathy. Another issue with diagnosing cancer patients using artificial intelligence is that the patient may feel restricted in transmitting their internal emotions and therefore, the specialist who is treating that individual may lose empathy which will potentially reduce the quality of care further (M Robson, 2018). It is believed by these professionals that AI cannot display empathy they way a doctor can and essentially can sabotage the bond between the patient and doctor.

In contrast to the notion that AI sabotages the patient-doctor bond, Dr Eric Topol’s book ‘Deep Medicine: How Artificial intelligence can make Healthcare human again’ presents an optimistic viewpoint of the future of algorithms and medicine. The relationship between patient and doctor has deteriorated over the years. Medicine has become so depersonalised and with this new technology individual patients can be deeply understood and given the care they need. The main point of this argument in his book was based on the idea that doctors are ‘burned out’ ‘tired’ and ‘depressed’ and now more than ever. In the UK, this could be due to increasing work and study hours with little pay. Doctors simply cannot sustain a friendly and comforting relationship with each and every one of their patients, especially if their role requires empathy like diagnosing skin cancer. Dr Eric Topol believes AI can help enhance the human aspect of diagnosis’ as AI can focus on the accuracy and efficiency of diagnosis therefore the doctor is able to focus on his/her patient as an individual.

After considering the reasons for and against the use of AI in diagnostics and its impact on the patient-doctor bond, it is debatable whether this bond is lost or resuscitated by AI.

Abstract

Among the three basic types of skin cancer, viz, Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC) and Melanoma, Melanoma is the most dangerous in which survival rate is very low. The proposed skin cancer detection technology is broadly divided into four basic cmponents, viz., image preprocessing which includes hair removal, de-noise, sharpening, resize of the given skin image, segmentation which is used for segmenting out the region of interest from the given image. Here we have used k-means segmentation. The classification algorithm which are going to be used here are Support Vector Machine (SVM).

Introduction

Among three types of skin cancer, viz., Squamous Cell Carcinoma (SCC), Melanoma and Basal Cell Carcinoma (BCC), Melanoma is most dangerous in which survival rate is very low. Early detection of Melanoma can potentially improve survival rate of victim. In USA, in every hour one person dies in melanoma. From a study, it is estimated that around 87,110 new cases of melanoma will be diagnosed in 2018. Among them, 9,730 will die because of melanoma. Melanoma consists of only 1% of all skin cancer cases but the majority of skin cancer death. The vast majority of melanomas are caused by the sun. From a survey done by a UK University, it is found that 86% of melanomas are exposed by ultraviolet (UV) radiation. On average, people’s risk for melanoma doubles if he or she has had more than five sunburns. If a person use SPF 15 or higher SPF sunscreen regularly it can reduce the risk of melanoma by 50% and squamous cell carcinoma by 40%[1].

Earlier Work

This is the scenario for which many projects have been tried and developed. Although not same but many related work have been done by many researchers. Some of papers have been referred and explored here. A detailed analysis of the existing systems is done. This study helped in identifying the benefits and also the drawbacks of existing systems.

[1] Enakshi Jana , Dr. Ravi Subban, S.Sarawathi, “Research on skin cancer cell detection using image processing”, 2017. In this paper, an extensive literature survey of current technology is made for skin cancer detection. Of all the methods used for skin cancer detection, SVM and Adaboost produces the best results. [2] Shivangi Jain, Vandana Jagtap, Nitin Pise, “Computer aided melanoma skin cancer detection using Image processing”, 2015. In this paper it is concluded that the proposed system can be effectively used by patients and physicians to diagnose the skin cancer more accurately. This tool is more useful for the rural areas where the experts in the medical field may not be available. [3] Vijayalkshmi M.M, “Melanoma skin cancer detection using Image processing and machine learning”, June 2019. The aim of this project is to determine the accurate prediction of skin cancer and also to classify the skin cancer as malignant or non-malignant melanoma. [4] Sanjana M, Dr. V. Hanuman Kumar, “Skin cancer detection using Machine learning algorithm”, Dec 2018. This paper focuses on determining the stage of the skin cancer, based on various feature such as the area of the spread, diameter, color of the lesion, etc. The analysis can be made with the help of machine learning algorithm.[5] Jianpeng Qi, Yanwei Yu*, Lihong Wang,and Jinglei Liu School of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, China, “K*-Means: An Effective and Efficient K-means Clustering Algorithm”, in 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom). [6]Ginu George, 2 Rinoy Mathew Oommen, 3 Shani Shelly, 4 Stephie Sara Philipose, 5 Ann Mary Varghese 1,2,3 UG Scholar, Department of Computer Science and Engineering, “A Survey on Various Median Filtering Techniques For Removal of Impulse Noise From Digital Image”, IEEE Conference on Emerging Devices and Smart Systems (ICEDSS 2018) 2-3 March 2018.

The system has two parts, training and testing. Both parts undergo following steps.

• Step 1] Our first step involves the collection of Images.For this purpose we have collected 5026 Dermoscopy Images taken from the dermoscope.
• Step 2]In the second step we are doing the image preprocessing. The process involved in the preprocessing are like hair removal, noise removal, enhancement, colour conversion. In hair removal algorithm we have used opening and closing algorithm for noise removal we have used median filter.For enhancement purpose we have used histogram. For training our model we needed all the images of the same dimension, so for that purpose we resised the images.
• Step 3] Third step is segmentation using k-mean clustering, , k-means heavily depends on the position of initial centers, and the chosen starting centers randomly may lead to poor quality of clustering. In our paper we have optimized k-means clustering method along with three optimization principles named k∗-means.

Segmentation is followed by feature extraction. No machine learning algorithm can work without predefined features set. The type of features can be broadly divided into following categories.

Shape Features – Asymmetry, Compactness, Ulnar Variance, Diameters. 2.Texture Features – GLCM (Gray-Level CoOccurrence Matrix), Coarseness. 3. Color Features – Variance, Entropy, Skewness. 4. Contrast – Measure of the local variations and texture of shadow depth. 5. Homogeneity – Measure of closeness of the distribution of elements. The feature of nucleus is extracted using WAVELET TRANSFORM and GLCM.

• Step 4] In training part features of pure cancer cell is stored in data base.

In the testing part, the cell which needs to be tested is taken as input.

• Step 5] Finally SVM classifier with the help of data in the data base is used for classification, where decision is done whether the cell is cancerous or not.

CONCLUSION

The proposed system of skin cancer detection can be implemented using support vector machine to classify easily whether image is cancerous or non-cancerous. The system will determine the stage of the skin cancer, based on various features such as the area of the spread, diameter, color of the lesion, etc. The analysis can be made with the help of the machine learning algorithm, in which we train the system based on the history of the images stored in the database, and the test image comes in the category of the melanoma or not, if it does, then to determine its stage. A comparison can be made with the existing systems, machine learning reduces the computational time. Hence, the treatment can begin faster.

REFERENCES

1. Enakshi Jana , Dr. Ravi Subban, S.Sarawathi, “Research on skin cancer cell detection using image processing”, 2017.
2. Shivangi Jain, Vandana Jagtap, Nitin Pise, “Computer aided melanoma skin cancer detection using Image processing”, 2015.
3. Vijayalkshmi M.M, “Melanoma skin cancer detection using Image processing and machine learning”, June 2019.
4. Sanjana M, Dr. V. Hanuman Kumar, “Skin cancer detection using Machine learning algorithm”, Dec 2018.
5. Jianpeng Qi, Yanwei Yu*, Lihong Wang,and Jinglei Liu School of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, China, “K*-Means: An Effective and Efficient K-means Clustering Algorithm”, in 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom).
6. Ginu George, 2 Rinoy Mathew Oommen, 3 Shani Shelly, 4 Stephie Sara Philipose, 5 Ann Mary Varghese 1,2,3 UG Scholar, Department of Computer Science and Engineering, “A Survey on Various Median Filtering Techniques For Removal of Impulse Noise From Digital Image”, IEEE Conference on Emerging Devices and Smart Systems (ICEDSS 2018) 2-3 March 2018.
7. https://medium.com/machine-learning-101/chapter-2-svm-support-vector-machine theory-f0812effc72
8. https://towardsdatascience.com/https-medium-com-pupalerushikesh-svm-f4b42800e989
9. Pratik Dubal, Sankirtan Bhat, Chaitanya Joglekar, Dr. Sonal Patil, “Skin cancer detection and classification”, 2017.
10. Mohd Afizi Mohd Shukran, Nor Suraya Mariam Ahmad, Farahana Rahmat, “Melanoma cancer diagnosis device using Image processing techniques”, Feb 2019.
11. Nay Chi lynn, Zin Mar Kyu, “Segmentation and classification of skin cancer melanoma from skin lesion images”, 2017.

This paper studies malignant melanoma, one of the most dangerous types of cancer in the world. It discusses its pathology and epidemiology and presents information about its incidence. The paper addresses risk factors associated with the disease, including sun exposure, as well as host and environmental factors. It also studies the role of genetics and personal behavior in melanoma acquisition. Finally, the paper discusses the role of healthcare services and self-diagnosis in disease prevention and decreased mortality rates.

Pathology and Epidemiology of Melanoma

Melanoma is the most rapidly growing type of cancer in the world, as well as the fifth leading cancer in men and the seventh in women in America (Nikolaou & Stratigos, 2014). There has been an increase in melanoma incidents over the past several decades. In the US, the incidence of the disease has grown from approximately 8,5 cases per 100,000 population in 1975 to around 35 cases in 2010 (Berwick et al., 2016). There is a globally observed dramatic increase of incidents in the 60 and more years old age group (Nikolaou & Stratigos, 2014). The number of melanoma cases does not correspond with mortality rates, which have remained stable since the 1980s. The reason for it is the trend for an expanded skin screening and earlier detection of the disease at a potentially curable stage. The location of melanoma on human bodies varies and depends on their age and is usually truncal for young people and can be found on the necks or heads of older people.

Nikolaou and Stratigos (2014) report that the highest incidence of the disease worldwide is presented in New Zealand and Australia, each having up to 60 newly diagnosed patients per 100,000 population annually. The male/female ratio differs among the countries, but the predominance of melanoma in men has been reported in countries with high disease incidence, including America. Most Northern and Western European countries, however, disclose a higher percentage of women having melanoma, while in Central Europe it prevails in men (Nikolaou & Stratigos, 2014). Both females and males should equally protect themselves from the disease.

Risk Factors

Sun Exposure

There are several risk factors associated with the disease; the primary one is sun exposure. In America, Canada, Australia, and Nordic countries, the proportion of melanoma incidents related to sun exposure is more than 90%, and between 80% and 90% in the majority of European countries (Berwick et al., 2016). The authors report that an irregular pattern of sun exposure can raise the risk of the disease. Short periods of sunbathing and other outdoor activities damage the skin as it is not protected from the sun. Intermittent sun exposure is most dangerous for humans as there is no epithelial thickening and tanning effect on the skin. At the same time, chronic sun exposure can have an adverse influence on humans, but it shows a weak association with melanoma because the skin receives extra protection with tan (Berwick et al., 2016). It is clear that people living in sunny areas are at higher risk of the disease; however, any individual can have melanoma.

Host Factors

Several groups of people are at higher risk of acquiring the disease than other ones. For example, fair-skinned individuals are more likely to acquire melanoma than people with darker skin are. The other risk factors include poor tanning ability, freckling, and multiple naevi, as well as dark hair color and light eye color (Nikolaou & Stratigos, 2014). It is crucial to mention that children are more sensitive to sun exposure than adults are, which makes them a risk group as well.

The number and type of nevi are primary host factors contributing to the development of the disease, as it modifies an individual’s response to sun exposure. Berwick et al. (2016) report that individuals with less than 15 nevi are at a seven times smaller risk of melanoma than the ones having more than 100 nevi. The size of the nevus is also significant, as it can increase the chances of disease as well. Atypical nevi are known to be the factors influencing the disease as well. Notably, increased sun exposure in the early years of life can result in a greater number of nevi and the occurrence of dysplastic ones (Berwick et al., 2016).

Environmental Factors

Several environmental factors are contributing to the disease as well. For example, Berwick et al. (2016) suggest that exposure to benzene and other chemicals usually used in printing, as well as to ionizing radiation, can contribute to the development of melanoma. Risk factors may also include exposure to chromium and organochlorine compounds, including chlorine-based pesticides (Berwick et al., 2016). It means that people working in factories and chemical or electronic industries are at higher risk of melanoma.

Role of Genetics

The study by reading, Wadt, and Hayward (2016) report that positive family history is related to an increased risk of the diseases. The researchers point out that although the primary causes of melanoma are randomly acquired mutations within melanocytes, the presence of a heritable gene can increase susceptibility to the disease significantly. The most common gene related to familial melanoma is the cyclin-dependent kinase inhibitor 2A (Read et al., 2016). Genetics can also contribute to other types of the disease, including pancreatic cancer. Moreover, melanoma risk genes may interact with other factors to affect the contraction of the disease. However, it is necessary to mention that no germline alternation guarantees the occurrence of melanoma.

Role of Personal Behaviors

Individuals’ behavior can have a crucial role in the disease. As exposure to the sun is the primary cause of melanoma, people need to be cautious during outdoor activities. Tan is perceived as an attribute of a healthy and wealthy individual; however, spending time under the sun can cause severe damages to health. Many people prefer not to use sun protection, including clothing items and sunscreen because they are not aware of the adverse consequences of sun exposure. Moreover, some individuals suppose that the sun is not dangerous to the darker skin. Such ignorance can lead to inevitable consequences and poor health conditions.

To reduce the risk of melanoma, individuals should utilize sun protection methods both in summer and in winter. Those methods may include using sunscreen, wearing a hat, and avoiding exposure to the sun during the day. Moreover, they should consider seeing a medical professional if they have multiple nevi or moles, especially if they have an atypical shape or size. Finally, individuals that are often exposed to the sun should undergo preventive skin screening to increase the chance of early diagnosis.

Diagnosis and the Role of Healthcare Services

The key factor in decreasing mortality is the early detection of malignant melanoma. Healthcare services in many countries, including America, Brazil, Australia, and others, have contributed to the lower level of incidence by promoting prevention methods and early diagnosis during campaigns. However, the topic remains acute, as many people cannot visit a medical professional for testing. For example, Escobedo et al. (2017) note that in the US, only 30% of the population receive insurance from public or government programs, which means that many Americans do not have access to care and, specifically, cannot utilize preventive methods, such as skin screening. Rastrelli, Tropea, Rossi, and Alaibac (2014) suggest that self-examination can be an essential part of the early diagnosis for those who cannot afford professional assistance. Individuals may use the ABCD (Asymmetry, Border Irregularity, Color variegation, Diameter) criteria or a Glasgow 7-point checklist to recognize the disease in its early stages.

For diagnosis, healthcare services use dermoscopy or epilumenescent microscopes as non-invasive methods of skin observation. Melanoma-specific criteria include atypical pigment network, irregular dots or globules, irregular streaks or pigmentation, regression structure, vascular pattern, and the blue-whitish veil (Rastrelli et al., 2014). Modern techniques of diagnosis also include total body photographic images and short-term surveillance. Unlike other types of cancer, malignant melanoma is located on a patient’s skin, which makes the early diagnosis possible.

Conclusion

Melanoma is a dangerous condition that has to be addressed and discussed as many people are at risk of the disease. It is mainly caused by sun exposure, although there are environmental and genetic factors that contribute to its acquisition. Melanoma can be prevented by the use of protection methods, avoiding sunbathing, and seeing a medical professional in case of multiple naevi. It is crucial to raise awareness of the risk factors to decrease the incidence of the disease.

References

Berwick, M., Buller, D. B., Cust, A., Gallagher, R., Lee, T. K., Meyskens, F.,… Ward, S. (2016). Melanoma epidemiology and prevention. In H. L. Kaufman & J. M. Mehnert (Eds.), Melanoma (pp. 17-49). Cham, Switzerland: Springer.

Escobedo, L. A., Crew, A., Eginli, A., Peng, D., Cousineau, M. R., & Cockburn, M. (2017). The role of spatially-derived access-to-care characteristics in melanoma prevention and control in Los Angeles County. Health & Place, 45, 160–172.

Nikolaou, V., & Stratigos, A. J. (2014). Emerging trends in the epidemiology of melanoma. British Journal of Dermatology, 170(1), 11-19.

Rastrelli, M., Tropea, S., Rossi, C. R., & Alaibac, M. (2014). Melanoma: Epidemiology, risk factors, pathogenesis, diagnosis and classification. In Vivo, 28(6), 1005-1012.

Read, J., Wadt, K. A., & Hayward, N. K. (2016). Melanoma genetics. Journal of Medical Genetics, 53(1), 1-14. Web.