Abstract
Brain tumor classification from MRI scans is a challenging task that requires accurate and timely detection increase patient survival rates. Conventional machine learning methods with hand-crafted features often fail to handle different sizes, forms, and textures of tumors. In this study evaluates, standard transfer learning models (AlexNet, MobileNetV2, InceptionV3, ResNet50, VGG16, VGG19) and conventional classifier models such as Decision Tree, Naïve Bayes, LDA were evaluated for multiclass brain tumor classification. The Vision Transformer (ViT) which leverages global context modeling achieved accuracy of 87.34%. To further improve performance, a hybrid CNN–ViT framework named CAFNet with data augmentation and a Cross-Attention Fusion mechanism achieving a test accuracy of 96.41% on a multiclass MRI dataset. The results show that CAFNet significantly outperforms conventional machine learning, deep learning and transfer learning models for robust brain tumor classification.