A Comprehensive Analysis of Machine Learning Techniques in Biomedical Image Processing Using Convolutional Neural Network

Deep learning is a branch of machine learning that has grown by leaps and bounds since it was first used in computer vision. The "Olympics" of computer vision, ImageNet Classification, was won by a system that used deep learning and convolutional neural networks in December 2012. Because of how important it is in the field, this competition is sometimes called the "Olympics" of computer vision. (CNN). Since then, people in many different fields, such as medical image analysis, have looked into deep learning. We are going to look into whether or not it would be possible to use deep learning algorithms to analyse medical images. This poll asked people what they thought about the four following topics related to machine learning: 1) How it is now used in computer vision, 2) How machine learning has changed before and after deep learning, 3) What role ML models play in deep learning, and 4) How deep learning can be used to analyse medical photos. Before the invention of deep learning, most machine learning systems relied on inputs called "features." This type of machine learning is called feature-based ML by some (also known as feature-based ML). Studying photographic data can be used to learn through deep learning without the need to separate objects or pull out features. The main difference between the two was this. This was pretty clear when we looked at MLs made before and after deep learning became very popular. This part, along with the model's huge scope, makes deep learning work well. Even though the term "deep learning" is still new, a study on the topic found that photo-input deep-learning algorithms have been available in the field of machine learning for a long time. Even though "deep learning" is a term that has only been around for a short time, this was seen. Even though the idea of "deep learning" is still in its early stages, discoveries like this one have been made. Even before the term "deep learning" was invented, machine learning techniques that used pictures as input were already showing promise for solving a wide range of medical image interpretation problems. Even before the term "deep learning" was made up, this was the case. One of these jobs is to Figure out how lesions are different from other organs and tissues. To solve the problem, an approach to machine learning that is based on images was used. In the next few decades, it is expected that deep learning will completely replace all of the traditional ways that medical images are currently interpreted. This is because applying deep learning and other machine learning techniques to the study of picture data could make medical image analysis much better. "Deep learning," which is the process of teaching computers to "learn" from images, is one of the most promising and quickly growing areas of medical image analysis. Traditional ways of figuring out what a medical image means are likely to be replaced in the next few decades by machine learning that works from pictures.

I. Introduction

The use of machine learning (ML) [1 –3] has a hugely positive effect on all parts of medical imaging. For example, medical image analysis, CAD [4–7], and radiomics all benefit greatly from the application of ML [1–3]. This is because even fundamental elements of medical imaging, such as tumours and organs, may be too complex to be depicted by a straightforward equation or model. This explains why everything is the way it is. While polyps in the colon seem like little balls, lesions in the colorectal region might look like flat areas [8, 9]. For an adequate description of the situation, a model with several parameters is required. There are too many variables for the calculation to be done manually. Due to the high degree of complexity in medical imaging model parameters, "learning from data" is a crucial technique. Therefore, it is not surprising that ML plays a significant role in diagnostic imaging for the medical field.