Brain Tumor Diagnosis using Machine Learning: A Review

Abstract

Recently, early brain tumor diagnosis has grown in importance as a study area recently. The patient's rate of survival rises with early tumor detection for primary treatment. Because of the high processing overhead caused by the enormous volume of image input to the processing system, processing magnetic resonance images (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 segmentation for accurate and faster processing. The latest literature has suggested the creation of novel methods depending on enhanced processing and learning for the detection of brain tumors. This essay provides a succinct overview of MRI-related advancements. The machine learning (ML) algorithms' capacity for fine processing and learning has shown an enhancement in the efficiency and accuracy of processing for the detection of a brain tumor in existing automation systems. Restrictions, advantages, and outlook for the future regarding the present approaches for computer-aided diagnostics (CAD) in the detection of brain tumors are discussed, along with current advances in automation related to brain tumor detection. In the presented study, we explore the history of numerous methods that have been put forth to image brain tumors across a variety of domains.