ADVANCED DEEP LEARNING AND MACHINE LEARNING TECHNIQUES FOR MRI BRAIN TUMOR ANALYSIS

Abstract

Brain tumors represent one of the most severe and life threating medical conditions globally, characterized by uncontrolled proliferation of cells within the intracranial cavity. Early and accurate diagnosis is paramount to prepare effective treatment schemes and significantly improve the patient's survival rates. Magnetic resonance imaging (MRI) has emerged as a better, non-invasive modality for neuroimaging due to its extraordinary soft-high-opposite and absence of ionization radiation. However, the manual interpretation of multi-home MRI versions is naturally prone to time-consuming, subjective and inter-reviewable variability. These challenges have catalyzed the development of automated computer-admitted diagnostic (CAD) systems, have taken advantage of progress in machine learning (ML) and, recently, deep learning (DL). This paper presents a systematic and comprehensive review of state -of -the -art ML and DL techniques applied to detection and classification of brain tumors from MRI data. We carefully analyze development from traditional ML functioning, which greatly rely on the facilitating facility extraction for modern deep nervous networks (CNN) that learn autonomically hierarchical facility representatives. The reviews include fundamental image preprocessing and segmentation techniques, a wide discovery of feature extraction methodology, and a significant evaluation of various classifiers, including support vector machines (SVMs), K-Nearest neighbor (K-NN), and Artificial Neural Network (ANNs). Custom CNN and transfer learning models (eg, VGG, Regent) have been given a significant emphasis on deep learning architecture, which has demonstrated remarkable performance benefits. In addition, we discuss extensive challenges such as limited public dataset, performance metrics, class imbalances, and overfitting, and including integration with AI (XAI), federated learning, and radiomics, to explain future research directions. By synthesizing findings from a comprehensive body of literature, the purpose of this review is to serve as a valuable resource for researchers and physicians.