Query-guided learning for efficient and interpretable multi-class brain tumor classification in MRI

Introduction: Accurate and efficient multi-class classification for brain tumors from MRI images is still a significant problem, requiring not only local feature learning but also global context comprehension. Methods: We present a novel Query-Guided Cross-Attention Convolutional Neural Network (QGC-CNN), which incorporates convolutional learning into a transformer-style network with learnable query tokens. The interaction between query tokens and feature maps helps discover long-range relationships that are difficult for standard CNNs to capture. To improve reliability and robustness, an ensemble method combining Xception, EfficientNetB0, ResNet50, and QGC-CNN was constructed, where the predicted values were combined using a LightGBM meta-learner. Results: The experiments show that the proposed framework achieved a test accuracy of 95.50% and an AUC of 0.99 for the "No Tumor" class. Importantly, the QGC-CNN model shows great efficiency, having just 0.83 GFLOPs, which is 10.9 times lower than that of Xception. Discussion: The proposed framework demonstrates that combining query-guided cross-attention with ensemble learning can provide accurate and computationally efficient brain tumor classification from MRI images.