Machine Learning in Medical Imaging

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Table of Contents

Abstract
Introduction
Installation
Machine learning in Medical Imaging
Medical Imaging Principles
Conclusion
References

Abstract

Artificial Intelligence (AI) has been in development in recent years and is on the rise. AI can be used in many fields, including machine learning. Machine learning uses computer science and engineering, which learn from data it receives and apply that knowledge to predict an outcome. This can be seen with medical imaging, as the machine is capable of interpreting images – faster than people – and identifying what could be wrong with that person. Machine learning utilizes a technique called convolutional neural networks (CNN) which is a type of deep learning. Deep learning just means the networks have more layers, which are important because they will help make medical imaging through machine learning more efficient or reliable as you keep adding layers to it. For example, computer software can look at someone’s X-ray and be able to use the algorithms built into it to identify and help diagnose a patient much quicker than a human could. It can take doctors years of experience to obtain adequate expertise for certain diagnoses. Machine learning can be used in mass to recognize problems that doctors might not have the time or experience to find.

Introduction

Machine learning (ML) traces its origin back in the artificial intelligence movement of the 1950s – ML was concerned mainly with the practical and application aspects of learning. As a branch of computer science, machine learning strives to ensure computers “can learn without being directly programmed” (Bi et al., 2019, p. 200). In fact, ML uses computer science and engineering, which learn from the data it receives and apply that knowledge to predict an outcome. Erickson et al. (2017) further defined machine learning as a technique that is used to recognize patterns that can be used in medical images. It is gaining popularity and use in the medical field as it helps to identify or diagnose patients faster than a human could.

Installation

In this section on machine learning, we will describe how machine learning will be implemented into different things. Descriptions will include the different types of machine learning that is used with a heavy emphasis on the medical field. One such example is deep learning (DL) which is used in object detection, segmentation and classification (Currie et al., 2019). For example, in object detection, the discussion will show how DL is used in location of a lesion. Our clarification of the basic processes of machine learning including supervised machine, unsupervised, semi and reinforcement learning provide context for our investigation of medical imaging, in the next section.

Machine learning in Medical Imaging

In this section medical imaging, we will describe the process of AI being installed in medical imaging. Descriptions will include where in the medical field the AI will be implemented, with an emphasis on the different types of imaging (Fu et al., 2019).For instance, the final discussion will strive to show how deep learning and machine learning techniques can be used to detect symptoms from X-ray, CT scan, and MRI images (Khalid et al., 2020 and Meyfroidt et al., 2009). Therefore, the clarification of the basic processes of the different topics of medical imaging, including X-rays, MRIs, and CT scans, will provide context for our investigation of machine learning in the next section.

Medical Imaging Principles

X-rays

In this section on medical imaging, we will describe how AI is used to diagnose or be able to understand X-ray images. It is important to note that AI played a critical role as a forecast method for detecting COVID-19 from chest x-ray images (Mohammad-Rahimi et al., 2021). Descriptions in this section will include what the AI will do when it sees these images, with an emphasis on how it is programmed to make a diagnosis. Similarly, a discussion on how various ML techniques such as Support vector and random forest as well as DL methods including Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) works will be provided (Meyfroidt et al., 2019). Our clarification of the basic processes of understanding an X-ray provides more context for our investigation on what AI can do with more advanced technology such as an MRI, in the next section.

MRIs

In this section on medical imaging, we will describe the use of AI in MRIs. More specifically, the discussion will show why some soft tissue tumors could not be identified from normal muscle with CT, but seen easily using MRI. The paper will also highlight some of the latest AI applications in MRI. Descriptions will further include how the AI will be installed, with an emphasis on how its program will make a diagnosis. Our clarification of the basic processes of understanding how MRIs work provides more context for our investigation of medical imaging in the next section.

CT Scans

In this section on medical imaging, we will describe the installation of AI into CT Scans. Descriptions will include how the AI will be installed, with an emphasis on what the program does to come up with a diagnosis. Similarly, an illustration of the processes in a CT examination will be discussed. This clarification of the basic process of understanding how MRIs work provides more context for our investigation of medical imaging.

Deep learning vs. machine learning

Machine and deep learning methods play an important role in analyzing different dimensional components of medical imaging. Although these two methods have proven effective in the medical field, they differ in some aspects. Firstly, deep learning methods require a huge amount of labeled training to make an accurate conclusion. Machine learning, on the other hand, “can apply a small amount of data delivered by users” (Mohammad-Rahimi et al., 2021, p. 234). Secondly, DL methods function well with high-performance hardware while ML needs features to be precisely branded by users. More specifically, ML programs, unlike DL can still function well on conventional computers – deep learning systems require powerful hardware which explains why graphical processing units are recommended. GPU ensures DL performs different operations simultaneously.

Thirdly, machine learning tend to categorize tasks into small fragments and further combine the obtained results into a single conclusion while deep learning resolves such problems with the help of end-to-end principles. In addition to this, ML is a type of AI that automatically work with minimum human interference while DL utilizes artificial neural networks to mirror how human brain work.Lastly, machine learning uses unstructured data which helps guide its algorithms while in deep learning, neural networks drives its algorithms.

Conclusion

What Machine Learning Offers

The use of AI has many advantages in the medical field and provides a promising future for diagnosing patients. For instance, AI played an important role in managing the spread of COVID-19 – it was used in the prediction of potential hotspots with the help of contact tracing. Contract tracing, as a control measure, helps reduce the spread of the disease – it contacts and informs individuals that they have been exposed to the virus. The different studies explicated above are focused on which types of medical imaging the machine learning will be implemented into including, X-rays, MRIs, and CT Scans. The medical field is always in need of improvement, especially with the process of diagnosing patients. Therefore, AI offers the field several applications that help a lot in end-to-end drug discovery, diagnosing patients, as well as improving communication within the healthcare facility. In other words, AI will help to make diagnoses quicker and more efficient than humans in the future with the use of programming, enabling it to detect things better.

References

Bi, Q., Goodman, K. E., Kaminsky, J., &Lessler, J. (2019). What is machine learning? A primer for the epidemiologist. American journal of epidemiology, 188(12), 2222-2239. Web.

Currie, G., Hawk, K. E., Rohren, E., Vial, A., & Klein, R. (2019). Machine learning and deep learning in medical imaging: Intelligent imaging. Journal of medical imaging and radiation sciences, 50(4), 477-487. Web.

Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017). Machine learning for medical imaging. Radiographics, 37(2), 505. Web.

Fu, G. S., Levin-Schwartz, Y., Lin, Q. H., & Zhang, D. (2019). Machine learning for medical imaging. Journal of healthcare engineering, 14(7), 344-400. Web.

Khalid, H., Hussain, M., Al Ghamdi, M. A., Khalid, T., Khalid, K., Khan, M. A., & Ahmed, A. (2020). A comparative systematic literature review on knee bone reports from MRI, X-rays and CT scans using deep learning and machine learning methodologies. Diagnostics, 10(8), 518-525. Web.

Meyfroidt, G., Güiza, F., Ramon, J., &Bruynooghe, M. (2009). Machine learning techniques to examine large patient databases. Best Practice & Research Clinical Anaesthesiology, 23(1), 127-143. Web.

Mohammad-Rahimi, H., Nadimi, M., Ghalyanchi-Langeroudi, A., Taheri, M., &Ghafouri-Fard, S. (2021). Application of machine learning in diagnosis of COVID-19 through X-ray and CT images: A scoping review. Frontiers in cardiovascular medicine, 8 (3) 63-80. Web.

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