DEEP LEARNING-DRIVEN MRI-BASED BRAIN TUMOR DETECTION AND GRADING: A CRITICAL ASSESSMENT AND PERFORMANCE ANALYSIS

Abstract

Brain tumors, both benign and malignant, pose significant health challenges, with early and accurate diagnosis being critical for effective treatment planning. Magnetic Resonance Imaging (MRI) remains the gold standard for non-invasive tumor assessment due to its high soft-tissue contrast and multiparametric capabilities. In recent years, deep learning (DL) techniques have revolutionized brain tumor detection, segmentation, and grading by surpassing traditional methods in accuracy and robustness. This survey provides a detailed review and comparative performance analysis of state-of-the-art DL and machine learning (ML) methods developed between 2022 and 2025. The study examines benchmark MRI datasets such as BraTS, TCGA, REMBRANDT, and Figshare, highlighting their relevance to clinical research. Comparative evaluation reveals the evolution from CNN-based architectures to hybrid CNN–Transformer frameworks and ensemble strategies, with recent models achieving near-perfect classification accuracy. Despite these advancements, challenges remain regarding model interpretability, computational efficiency, and generalization to heterogeneous clinical data. The assessment concludes by identifying research gaps and outlining future directions for lightweight, explainable, and clinically deployable AI solutions in neuro-oncology.