Shahrood University of Technology Journal of AI and Data Mining 2322-5211 14 1 2026 01 01 A Hybrid Approach for Brain Tumor Classification: Enhancing MRI-Based Diagnosis with CNN-Transformer Synergy 37 49 3677 10.22044/jadm.2025.16554.2779 EN Samira Mavaddati Electronic Department, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran. Journal Article 2025 07 22 Brain tumors are among the most life-threatening neurological conditions, requiring precise and early diagnosis for effective treatment planning. Traditional deep learning models, such as Convolutional Neural Networks (CNNs) and ResNet-based architectures, have demonstrated promising results in brain tumor classification. However, these models often struggle to capture long-range dependencies within MRI images, which are crucial for accurate classification. To overcome this limitation, we propose a Hybrid CNN-ViT model, combining the strengths of Vision Transformers (ViT) and CNNs to achieve high-precision brain tumor classification. The CNN component effectively extracts local spatial features, while the ViT module captures global contextual relationships within MRI scans. The model is evaluated on a four-class dataset of Glioma, Meningioma, Pituitary tumors, and non-tumor images, achieving an impressive accuracy of 98.37%, surpassing conventional CNN-based methods. By leveraging transfer learning, the approach enhances classification performance while reducing reliance on large-scale labeled datasets. The proposed Hybrid CNN-ViT model offers a scalable, robust, and efficient solution for real-world neuro-oncological diagnostics, significantly improving the accuracy of MRI-based brain tumor detection. https://jad.shahroodut.ac.ir/article_3677_2520825a4d4f3e1f0a05de6e45535671.pdf