A Comprehensive Survey of Machine Learning Applications in Medical Image Analysis for Artificial Vision

Abstract

This study presents a thorough survey of the applications of machine learning in medical image analysis for artificial vision,aiming to offer a comprehensive understanding of the evolving intersection between machine learning and medical imaging.With the rapid advancement of artificial vision technologies, the integration of machine learning algorithms has becomepivotal in revolutionizing medical image analysis. The survey explores a diverse range of machine learning applicationswithin the medical imaging domain, encompassing techniques such as convolutional neural networks (CNNs), support vectormachines, and decision trees. The focus lies in elucidating the role of machine learning in enhancing the accuracy, efficiency,and diagnostic capabilities of medical image analysis systems. Key topics addressed in the survey include image segmentation, classification, and detection, with a specific emphasis on applications in radiology, pathology, and ophthalmology. Additionally, the survey discusses challenges and opportunities in the integration of machine learning into medical image analysis, providing insights into current trends and future directions. This comprehensive survey serves as a valuable resource for researchers, practitioners, and healthcare professionals seeking an in-depth overview of the diverse applications and evolving landscape of machine learning in medical image analysis for artificial vision.