Brain cancer is the deadliest cancer that occurs in the brain and central nervous system, and rapid and precise grading is essential to reduce patient suffering and improve survival. Traditional convolutional neural network (CNN)-based computer-aided diagnosis algorithms cannot fully utilize the global information of pathology images, and the recently popular vision transformer (ViT) model does not focus enough on the local details of pathology images, both of which lead to a lack of precision in the focus of the model and a lack of accuracy in the grading of brain cancer. To solve this problem, we propose an adaptive sparse interaction ResNet-ViT dual-branch network (ASI-DBNet). First, we design the ResNet-ViT parallel structure to simultaneously capture and retain the local and global information of pathology images. Second, we design the adaptive sparse interaction block (ASIB) to interact the ResNet branch with the ViT branch. Furthermore, we introduce the attention mechanism in ASIB to adaptively filter the redundant information from the dual branches during the interaction so that the feature maps delivered during the interaction are more beneficial. Intensive experiments have shown that ASI-DBNet performs best in various baseline and SOTA models, with 95.24% accuracy in four grades. In particular, for brain tumors with a high degree of deterioration (Grade III and Grade IV), the highest diagnostic accuracies achieved by ASI-DBNet are 97.93% and 96.28%, respectively, which is of great clinical significance. Meanwhile, the gradient-weighted class activation map (Grad_cam) and attention rollout visualization mechanisms are utilized to visualize the working logic behind the model, and the resulting feature maps highlight the important distinguishing features related to the diagnosis. Therefore, the interpretability and confidence of the model are improved, which is of great value for the clinical diagnosis of brain cancer.
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