An Intensive Review of Brain Tumor Detection from MR Images using Machine Learning and Deep Learning Networks

The use of Magnetic Resonance Imaging (MRI) scan techniques in the field of Brain tumor detection is the main topic of this review study, which explores the changing field of brain tumor detection. Brain tumors are a serious medical problem that require early and precise discovery in order to effectively treat. The introduction highlights the frequency of brain tumors and highlights the critical role that MRI scans serve in providing non-invasive, fine-grained soft tissue imaging. The paper critically addresses the challenges inherent in brain tumor detection, ranging from tumor size and location variability to limitations associated with conventional MRI methodologies. The study devotes a significant amount of space to the amalgamation of automated image processing and machine learning methodologies, specifically stressing the application of diverse machine learning models for automated identification of brain tumors in magnetic resonance imaging images. The investigation includes a review of datasets that are often used in research as well as approaches that have been adopted by more recent investigations. Comparative evaluation of several models and algorithms reveals the advantages, disadvantages, and performance indicators of each. The study concludes with an investigation of possible future paths, considering new technology and multimodal imaging strategies that may improve the precision and effectiveness of MRI scans in the identification of brain tumors. In conclusion, this comprehensive review not only synthesizes existing knowledge but also underscores the imperative for ongoing research to advance the field and improve outcomes for individuals grappling with brain tumors