Brain Cancer Detection With MRI and ML

Brain based cancer is a devastating and potentially fatal disease that demands efficient & timely diagnosis. MRI magnetic resonance-imaging has come as a very important utility for early detection and evaluation of tumors located inside the brain. In past few years, machine learning techniques (MLT) has played pivotal role in enhancing the diagnostic accuracy of brain cancer detection from MRI scans. This paper presents a deep review of many papers focused on integration of MRI and machine learning for brain cancer detection. Our primary objective is to evaluate the advancements and identify gaps within these studies to determine the most effective ML algorithm for building an optimal brain cancer detection model. Through a systematic analysis of the selected research papers, we assess the methodologies, datasets, and outcomes of various ML approaches, while considering their strengths and limitations. By comparing and contrasting the methodologies and results of the surveyed papers, we aim to make informed recommendations on the choice of an ML algorithm for brain cancer detection. Our review seeks to contribute to the ongoing efforts to improve early-stage detection and management of brain tumors, emphasizing pivotal role of machine learning in enhancing the medical techniques for imaging.