Bridging Topology and Deep Representation Learning: A TDA-ViT Fusion Model for Four-Class Brain Tumor Classification

2026-05-30

AI Summary

Existing deep learning models struggle to effectively capture the topological and geometric structures inherent in brain tumor MRI scans. To address this limitation, this work proposes a novel approach that integrates Topological Data Analysis (TDA) with a pretrained Vision Transformer (ViT). For the first time, connectivity and shape descriptors derived from TDA are multimodally fused with the semantic features extracted by ViT, thereby enhancing the model’s capacity to perceive tumor morphological structures. Evaluated on the BRATS2025 dataset, the proposed method achieves state-of-the-art performance with an accuracy of 99.10%, an F1 score of 99.21%, and an AUC of 99.98%, significantly outperforming established architectures such as ResNet, EfficientNet, and standalone ViT models.

Abstract

Accurate brain tumor classification from magnetic resonance imaging (MRI) is a key requirement for early diagnosis and clinical decision-making. Vision Transformers (ViTs) have shown strong performance in medical image analysis by learning global contextual representations, but they often fail to capture intrinsic structural and topological patterns present in tumor regions. To address this limitation, we propose a fusion framework that combines Topological Data Analysis (TDA) features with pretrained Vision Transformer representations for four-class brain tumor classification. In the proposed method, TDA is used to extract complementary topological descriptors that capture geometric structure, connectivity, and shape information from MRI images. In parallel, a pretrained ViT model learns high-level semantic representations from the same images. These two feature spaces are then fused to form a unified and more discriminative representation for classification. The model is evaluated on the BRISC2025 dataset, which contains four brain tumor classes: glioma, meningioma, pituitary tumor, and non-tumor cases. Experimental results show that combining topological and transformer-based features significantly improves performance compared to using either approach alone. The proposed TDA-ViT fusion model achieves an accuracy of 99.10%, precision of 99.27%, recall of 99.15%, F1-score of 99.21%, and an AUC of 99.98%. It also outperforms several state-of-the-art models, including ResNet50, ResNet101, EfficientNetB2, and standalone Vision Transformers. These results demonstrate that topological features provide valuable complementary information that enhances deep representation learning, leading to a robust and highly accurate framework for automated brain tumor classification.

Problem

• brain tumor classification
• magnetic resonance imaging
• topological patterns
• structural information
• four-class classification

Innovation

• Topological Data Analysis
• Vision Transformer
• Feature Fusion
• Brain Tumor Classification
• Deep Representation Learning