  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.
