Current Trends and Challenges in Machine Learning-Based Renal Segmentation: A Review

Authors- P.G Scholar Akbar Nagani, Assistant Professor Abhay Rewatkar

Abstract-The significance of renal segmentation in medical imaging—especially in contrast-enhanced computed tomography (CT) scans—for the diagnosis and treatment of renal disorders is discussed in this work. This segmentation procedure is now much more accurate and efficient thanks to the quick development of machine learning algorithms. Through an analysis of several machine learning techniques used in renal segmentation, this literature review identifies important discoveries and their consequences. It looks at transfer learning, semi-supervised learning, hybrid and multimodal approaches, deep learning tactics, and traditional machine learning techniques. It also covers the issues at hand as well as potential avenues for future research in this area.

DOI: /10.61463/ijset.vol.12.issue6.348