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Brain tumors can be life-threatening. Early detection and accurate determination of the type and location of brain tumors are crucial for intervening in the condition of brain tumor patients and saving lives. In areas with limited medical facilities and doctor resources, obtaining timely diagnosis from medical experts can be challenging, resulting in missed treatment opportunities. The use of the medical Internet of Things for remote diagnosis of brain tumors, which includes automatic diagnosis, is a solution to the global issue of imbalanced distribution of equipment and expert resources. Therefore, it is crucial to develop a robust and highly accurate intelligent diagnosis system for brain tumors. Acquiring and annotating brain tumor MR image data is a time-consuming and expensive process due to the large image sizes and limited sample annotation data available. These factors pose significant challenges to the robustness and accuracy of the model. To address these issues, we propose a new deep semi-supervised learning approach that fully utilizes unlabeled sample data. Data augmentation methods were used in the model to increase training data, avoid overfitting, and improve the model's generalization ability. A new sliding window feature extraction method was used to avoid resizing images, which may lead to loss and neglect of small features, in order to accurately diagnose brain tissue lesions of small brain tumors that are difficult to recognize. The feature extraction backbone network introduced the Convolutional Block Attention Module (CBAM) attention mechanism, which enabled the network to fully understand image information in both spatial and channel aspects, enhancing the network's perception ability of key features. Experiments were conducted on publicly available brain tumor datasets to validate the advantages of our method.
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