Machine Learning-Based Classification of Medical Images for Disease Diagnosis in Healthcare
Keywords:
Machine learning, deep convolutional neural networks (CNN), Computer Vision (CV)Abstract
Recent advances in Machine learning (ML)and deep convolutional neural networks(CNN) have revolutionised computer vision (CV) and picture analysis and comprehension. Classifying and segmenting medical pictures, as well as locating and detecting things of interest, have gotten much easier to do. There are a wide variety of medical applications that might benefit from CV, and this development could speed up their development and implementation. However, only a small number of actual implementations can be found in busy hospitals and clinics. In this article, we take a look at where CV is right now in the context of the healthcare industry. To help speed up research, development, and deployment of CV applications in health practises, we examine the major problems in CV and intelligent data-driven medical applications and offer future approaches. To begin, we conduct a comprehensive literature review in the CV area, focusing on works that classify medical pictures, recognise shapes and objects in images, and segment images for medical purposes. Second, we provide a detailed overview of the many obstacles that stand in the way of advancing the study, creation, and implementation of intelligent CV approaches in practical medical applications and Hospitals
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