Enhancing Brain Tumor Classification Using Vision Transformers with Colormap-Based Feature Representation on BRISC2025 Dataset

TL;DR

This paper introduces a novel deep learning framework combining Vision Transformers with colormap-based features to significantly improve multi-class brain tumor classification accuracy on MRI scans, demonstrating superior performance over traditional CNNs.

Contribution

The study presents a new approach integrating colormap-based feature representation with Vision Transformers for enhanced brain tumor classification from MRI images.

Findings

Achieved 98.90% classification accuracy on BRISC2025 dataset.

Model outperformed baseline CNN models like ResNet and EfficientNet.

Demonstrated high generalization with 99.97% AUC across classes.

Abstract

Accurate classification of brain tumors from magnetic resonance imaging (MRI) plays a critical role in early diagnosis and effective treatment planning. In this study, we propose a deep learning framework based on Vision Transformers (ViT) enhanced with colormap-based feature representation to improve multi-class brain tumor classification performance. The proposed approach leverages the ability of transformer architectures to capture long-range dependencies while incorporating color mapping techniques to emphasize important structural and intensity variations within MRI scans. Experiments are conducted on the BRISC2025 dataset, which includes four classes: glioma, meningioma, pituitary tumor, and non-tumor cases. The model is trained and evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The proposed method achieves a classification accuracy of 98.90%, outperforming baseline convolutional neural network models including ResNet50, ResNet101, and EfficientNetB2. In addition, the model demonstrates strong generalization capability with an AUC of 99.97%, indicating high discriminative performance across all classes. These results highlight the effectiveness of combining Vision Transformers with colormap-based feature enhancement for accurate and robust brain tumor classification and suggest strong potential for clinical decision support applications.