Comparative Analysis of CNN, ResNet50, and Vision Transformer Architectures for Brain Tumor Classification from MRI Images - Matthieu Kayembe; Franklin Mwamba; Pierre Kafunda; Fiston Oshasha; John Poma (Journal of Applied Informatics and Computing, 2026)
The classification of brain tumors from Magnetic Resonance Imaging (MRI) is a crucial task in computer-aided medical diagnosis. Recent advances in deep learning have significantly improved performance in this domain. In this work, a comparative analysis of three architectures is conducted: a Convolutional Neural Network (CNN) trained from scratch, a transfer learning-based model using ResNet50, and a Vision Transformer (ViT). The models are evaluated on a multi-class dataset containing four categories: glioma, meningioma, pituitary tumor, and no tumor. Experimental results show that the CNN achieves limited performance with moderate generalization capability. The ResNet50 model reaches high accuracy during training but suffers from severe overfitting, leading to a significant drop in performance on the test set. In contrast, the Vision Transformer achieves the best overall performance, with a test accuracy of 0.76 and a good balance between precision and recall. These results highlight the effectiveness of Transformer-based architectures for complex medical image classification tasks.