AI to Explainable Deep Learning in MRI-Based Brain Tumor Detection: A Comprehensive Review

Magnetic Resonance Imaging (MRI) driven brain tumor analysis has progressed rapidly with the emergence of advanced artificial intelligence techniques, particularly deep neural networks, transfer learning paradigms, and attention-guided architectures. Although these approaches achieve high performance in segmentation and classification tasks, persistent limitations remain in interpretability, cross-domain robustness, dataset imbalance, and clinical implementation. This review synthesizes recent developments in MRI-based tumor detection, spanning classical machine learning pipelines, convolutional and transformer architectures, ensemble strategies, domain adaptation frameworks, and Explainable Artificial Intelligence (XAI) methodologies. A clinically oriented unified framework is outlined to connect experimental research models with deployable hospital systems. Comparative discussions of datasets, evaluation metrics, architectural trends, and open research challenges are provided to support the design of scalable, interpretable, and clinically reliable AI systems for neuro-oncology.