Abstract

Medical image analysis, often known as MIA, is a subfield of the artificial intelligence discipline of computer vision. This discipline started to take shape in the 1990s by using mathematical methods to solve problems with non-medical picture analysis. For clinicians, medical photographs are a valuable source of information. Medical image analysis is changing as a result of the development of Deep Learning and the use of its approaches. Accuracy and precision of medical image diagnostic is always a concern in the medical field. The recent development of Deep learning in MIA has opened vistas for different exploration and experimentation. In recent years, machine learning has become more prevalent in processing, analysing and diagnosing medical images. The main advantage of machine learning is its ability to analyse and analyse medical images by algorithmically utilising the hierarchical relationship inside data. Machine learning is the leading research topic in medical imaging analysis because it uses artificial intelligence as a core system because of its data structure and related labelling characteristics. It relies on two essential components: first, the analysis of medical pictures using data science concepts and methodologies to offer exact patient metrics, and second, human-AI interactions in which AI actors aid patients. With the ability to interpret medical images using deep learning (DL) methods, radiologists and other professionals now have access to various options and innovations. We have categorized and summarized the methods and technological framework when adopting deep learning approaches. Analysis of medical images.

Keywords:

Medical Image Augmentation, Enhancement, Machine Learning, Deep Learning

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Mohankumar.S, Kavita Bhatt.,(2022). Medical Image Augmentation and Enhancement using Machine Learning and Deep Learning (1st ed., pp. 1-148). Jupiter Publications consortium,ISBN:978-93-91303-40-2, DOI: https://doi.org/10.47715/JPC.978-93-91303-40-2