Deep Learning Architectures for Brain Tumor Classification: A Comprehensive Investigation of Advances, Challenges and Clinical Translation

Abstract

Brain tumor categorization from medical pictures is still a difficult and skill-dependent endeavor for radiologists, despite the fact that it is vital for efficient planning of treatment and better patient outcomes. The pressing demand for more dependable and effective diagnostic tools is addressed by this review, which thoroughly examines cutting-edge Deep Learning (DL) techniques used to automate brain tumor classification. This paper systematically examined key DL architectures employed in this domain, including Convolutional Neural Networks (CNNs) – highlighting hierarchical frameworks and Bayesian optimization approaches, Capsule Networks (CapsNets) – focusing on boundary-guided and Bayesian variants like BayesCap for uncertainty quantification, and Vision Transformers (ViTs) – particularly ensembled models leveraging multi-head self-attention. The analysis highlights the remarkable accuracies (e.g., over 98% in multiple experiments) attained by these sophisticated methodologies, synthesizing evidence on model performance. The review also critically examines the main obstacles to clinical adoption, including the restrictive interpretability of "black-box" models, the lack of expert-annotated data and the challenge of obtaining multidisciplinary cooperation for widespread implementation, problems with model applicability across various datasets and imaging protocols, and strict patient data privacy concerns. It can be concluded that while DL offers immense potential for revolutionizing brain tumor diagnosis, future research must prioritize developing explainable Artificial Intelligence (AI) techniques, robust domain adaptation methods, efficient lightweight models, and privacy-preserving frameworks like federated learning to enable trustworthy and widespread clinical implementation.