Brain Tumor Diagnosis using Machine Learning: A Review

Recently, early brain tumor diagnosis has grown in importance as a study area recently. The patient's rateof survival rises with early tumor detection for primarytreatment. Because ofthe high processing overhead caused by the enormous volume regardingimage input to processing system, processing magnetic resonance image (MRI) for the early detection of tumors presents a problem. This led to a significant delay and a decline in system effectiveness. As a result, recently, there has been an increased requirement for an improved detection system for precise representation and segmentationfor accurate and fasterprocessing. Latestliterature has suggested the creation of novel methods depending onenhanced processing and learningfor the detection of brain tumors. This essay provides a succinct overview of the MRI-related advancements. The machine learning (ML)algorithms' capacity for fine processing and learninghas shown an enhancementin the efficiency and accuracy of processing for the detection of the brain tumor in existing automation systems. Restrictions, advantages,and outlook for future regardingthe present approaches for computer-aided diagnostics (CAD)in the detection of the brain tumor are discussed, along with current advances in automation related tobrain tumor detections. In the presented study, researcherexplore the history of numerous methods that have been put forth to image brain tumors across a variety of domains.