This study presents a deep learning framework for multi-class brain tumor classification from magnetic resonance imaging scans using Vision Transformers enhanced with colormap-based feature representation. The proposed method leverages transformer architectures to capture long-range dependencies while incorporating color mapping techniques to emphasize structural and intensity variations in MRI scans. Experiments on the BRISC2025 dataset containing glioma, meningioma, pituitary tumor, and non-tumor cases demonstrate superior performance with 98.90% accuracy, significantly outperforming baseline approaches. The framework utilizes standard metrics including accuracy, precision, recall, F1-score, and AUC for comprehensive evaluation.