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

Faisal Ahmed

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.

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

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.
Paper Structure (15 sections, 17 equations, 3 figures, 2 tables, 1 algorithm)

This paper contains 15 sections, 17 equations, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodology
  4. Data Preparation and Colormap Transformation
  5. Dataset Construction
  6. Vision Transformer Architecture
  7. Training Strategy
  8. Evaluation Metrics
  9. Hyperparameter Settings
  10. Experiment
  11. Datasets
  12. Experimental Setup
  13. Results
  14. Discussion
  15. Conclusion

Figures (3)

  • Figure 1: Representative MRI samples from the BRISC2025 dataset showing the four classification categories: non-tumor, pituitary tumor, meningioma tumor, and glioma tumor. All images are resized to a uniform spatial resolution for consistent model input.
  • Figure 2: Proposed colormap-enhanced Vision Transformer (ViT) framework for brain tumor classification. The pipeline begins with a grayscale MRI image, which is transformed into a pseudo-color representation to enhance structural patterns and intensity variations. The color-enhanced image is resized to a fixed resolution and divided into non-overlapping patches. Each patch is flattened and linearly projected into a latent embedding space, followed by the addition of positional embeddings and a learnable classification token. The resulting sequence of embedded patches is processed through multiple Transformer encoder layers consisting of multi-head self-attention and feed-forward networks. The final representation of the classification token is passed to a multilayer perceptron (MLP) head to generate the probability distribution over the four tumor classes.
  • Figure 3: Evaluation of TDA+DenseNet121 on the OASIS-1 dataset. (a) One-vs-rest ROC curves illustrating discrimination performance across the four Alzheimer’s disease classes. (b) Confusion matrix showing correct and misclassified instances for each class.