A Comprehensive Review on Machine Learning Methods for Categorizing Liver Tumors

Abstract

This comprehensive review delves into the application of machine learning methods for the categorization of liver tumors, offering a thorough examination of the current landscape in medical imaging and diagnostics. The escalating prevalence of liver tumors necessitates precise and efficient classification methods, and this paper systematically explores the diverse array of machine learning techniques employed in this context. From traditional approaches such as support vector machines and decision trees to more advanced deep learning algorithms, the review synthesizes existing literature to provide a holistic understanding of their strengths, limitations, and comparative performances. Furthermore, the article discusses key challenges in the domain, such as data scarcity and interpretability, proposing potential avenues for future research and innovation. With a focus on bridging the gap between clinical needs and technological advancements, this review contributes valuable insights to the evolving field of medical imaging, offering a roadmap for the development of robust and clinically relevant liver tumor classification systems.