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Abstract
Detecting brain tumors at an early stage through Magnetic Resonance Imaging (MRI) is essential for timely treatment and better survival outcomes. With the rapid progress of artificial intelligence, deep learning methods particularly Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) are increasingly used to automate tumor analysis by extracting features directly from MRI scans. This review evaluates 25 peer-reviewed articles published between 2019 and 2025 that applied these models for tumor detection, segmentation, and classification. Most studies show CNNs as the preferred choice, often reporting diagnostic accuracies above 90%. However, emerging evidence indicates that ViTs perform competitively, and sometimes better, on large-scale datasets due to their capacity to model global spatial relations through attention mechanisms. Despite these advances, issues such as small or unbalanced datasets, high computational requirements, overfitting, and limited interpretability remain barriers to clinical use. This paper summarizes key methodological developments, compares CNN and ViT performance, and highlights current obstacles. It concludes with practical recommendations for creating more robust and clinically relevant systems.
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References
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