2024/06/25 by Mohammad Ali Labbaf Khaniki, Khaniki, Mohammad Ali Labbaf, Mirzaeibonehkhater, Marzieh +3 · 2 citations
Neuroscience · #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.17670
openalex publication_date 2024/06/25 · openalex created_date 2024/06/27 · openalex updated_date 2026/07/28
Brain tumor classification is a challenging task in medical image analysis. In this paper, we propose a novel approach to brain tumor classification using a vision transformer with a novel cross-attention mechanism. Our approach leverages the strengths of transformers in modeling long-range dependencies and multi-scale feature fusion. We introduce two new mechanisms to improve the performance of the cross-attention fusion module: Feature Calibration Mechanism (FCM) and Selective Cross-Attention (SCA). FCM calibrates the features from different branches to make them more compatible, while SCA selectively attends to the most informative features. Our experiments demonstrate that the proposed approach outperforms other state-of-the-art methods in brain tumor classification, achieving improved accuracy and efficiency. The proposed FCM and SCA mechanisms can be easily integrated into other vision transformer architectures, making them a promising direction for future research in medical image analysis. Experimental results confirm that our approach surpasses existing methods, achieving state-of-the-art performance in brain tumor classification tasks.