From CNNs to Transformers: A Review of Evolving Deep Learning Architectures for Brain Tumor Classification

Accurate brain tumor classification is critical for patient prognosis and treatment planning, yet manual interpretation of medical images like MRI is subject to variability. Deep learning has emerged as a powerful tool for this task. This review charts the evolution of deep learning architectures for brain tumor classification. We conducted a comprehensive literature review, focusing on the architectural progression from foundational Convolutional Neural Networks (CNNs) to modern attention-based Transformer models. Key datasets, evaluation metrics, and clinical challenges are synthesized. The review details the trajectory from early CNNs (e.g., AlexNet, VGG), which excelled at local feature extraction, to advanced variants like ResNet, U-Net, and DenseNet that improved performance and enabled segmentation-classification workflows. The paradigm then shifted to Vision Transformers (ViT, Swin Transformer) and hybrid models, which explicitly model long-range dependencies and global context, often achieving state-of-the-art results. Challenges such as domain shift, data scarcity, and the need for explainability (XAI) are persistent themes. While both CNNs and Transformers have demonstrated high accuracy, the current state-of-the-art often involves hybrid architectures that leverage the strengths of both. Future progress lies in developing generalizable, efficient, and trustworthy models through techniques like self-supervised and federated learning, multimodal data fusion, and the development of large-scale medical foundation models, ultimately aiming to empower clinicians and improve patient outcomes.