Abstract
Brain tumors remain a major neurological challenge, where timely and accurate diagnosis is critical for improving patient outcomes. Although several reviews have examined machine learning (ML) and deep learning (DL) techniques for brain tumor analysis, most existing surveys either focus on a single methodological family or lack a comparative perspective across emerging computational paradigms. This review addresses that gap by providing an integrated analysis of ML, Convolutional Neural Networks (CNNs), Transformer-based models, Generative Adversarial Networks (GANs), and hybrid ensemble frameworks for tumor detection, classification, and segmentation using magnetic resonance imaging (MRI). Unlike prior reviews, we systematically evaluate the clinical applicability, dataset limitations, and reproducibility concerns of these models while identifying unresolved issues such as interpretability, data scarcity, and domain generalization. Furthermore, we synthesize trends in multimodal learning, federated frameworks, and explainable AI, offering actionable insights for translating research advances into clinical practice. This critical perspective highlights not only the state of the art but also the pathways required for developing robust, transparent, and clinically viable artificial intelligence (AI)-driven diagnostic systems.
