Machine Learning and Deep Learning for Brain Tumor Diagnosis and Classification: Methodologies, Challenges, and Future Directions

Brain tumors constitute a heterogeneous and life-threatening group of neurological disorders in which timely and accurate diagnosis is critical to improving patient outcomes.Conventional diagnostic practices, which rely heavily on manual interpretation of medical imaging, remain constrained by inter-observer variability, subjective judgment, and limited reproducibility, particularly when assigning tumor grades according to the World Health Organization (WHO) classification system.In recent years, machine learning (ML) and deep learning (DL) have emerged as transformative computational paradigms capable of automating complex pattern recognition in neuroimaging and enhancing diagnostic precision, efficiency, and consistency.A comprehensive review of ML/DL-based approaches for brain tumor analysis is presented in this study, encompassing key methodologies developed for tumor detection, segmentation, and classification across WHO grades.Despite notable research advances, clinical adoption remains impeded by several critical challenges, including insufficient dataset size and heterogeneity, a lack of model interpretability, limited generalizability across imaging acquisition protocols, and barriers associated with clinical integration and regulatory approval.Addressing these obstacles will require the development of large-scale, standardized, and multi-institutional datasets; the advancement of explainable artificial intelligence (XAI) frameworks to enhance clinical trust; and the incorporation of multi-modal patient data to improve diagnostic robustness.Furthermore, the convergence of ML/DL with emerging technologies such as blockchain and the Internet of Things (IoT) holds promise for enabling privacy-preserving, interoperable, and real-time diagnostic platforms.With continued advancements in algorithmic robustness, interpretability, and cross-institutional validation, ML/DL-based frameworks hold the potential to revolutionize brain tumor diagnosis and classification, ultimately improving diagnostic precision, prognostic assessment, and personalized treatment planning.