Automated Segmentation of Brain Tumors in MRI: A Comprehensive Review of Deep Learning Approaches

Abstract

The segmentation of tumor region from brain MRI images is challenging task. Automated approaches are necessary because manual segmentation takes a lot of effort and susceptible to inter-observer variability. This research provides an extensive analysis of deep learning methods for brain tumor segmentation from MRI data. The study begins with a summary of brain tumors and conventional segmentation techniques before doing a thorough evaluation of current developments in automated segmentation methods. We cover many deep learning architectures and their derivatives, specifically designed for brain tumor segmentation. The review also includes methods for differentiating between healthy brain tissue and aberrant tumor tissue. This paper critically examines the benefits, drawbacks, and potential applications of deep learning-based algorithms. It offers insights into current techniques and suggests future prospects for this important field of study.