An Experimental Analysis of Opportunities, Challenges, Concepts on Medical Image Processing

Medical image processing (MIP) is essential to contemporary healthcare because it makes it possible to extract vital diagnostic data from a various imaging techniques. In this study, the potential, difficulties, and fundamental ideas when it comes to medical imaging processing are experimentally analyzed. We investigate the various applications, the possibility for automation through cutting-edge technology, the underlying difficulties, and the underlying theoretical principles that underlie this dynamic sector. We provide insights into the current status and potential paths of medical image processing by a thorough analysis of recent research and experiments, with the goal of enhancing its capabilities and impact on patient care. By starting with theoretical underpinnings and working toward applications, this paper aims to provide an accessible beginning to deep learning for MIP. The widespread acceptance of deep learning is first discussed generally, encompassing various significant advances in computer science. By doing this, we may comprehend the causes of deep learning’s ascent across a variety of application fields. Naturally, one of these fields that have been significantly impacted by this quick advancement is MIP, particularly in image registration, image detection and identification, computer-aided diagnostics, and image segmentation.