关键词: 脑肿瘤分类, 深度学习, 卷积神经网络, 阈值化处理, 类平衡, EfficientNet, ECA注意力机制, CBAM注意力机制

Abstract:

In view of the problems of high complexity and low recognition rate of existing brain tumor classification models and methods, an model based on improved EfficientNet-B0 is proposed for the classification of three brain tumors. In the data preprocessing stage, the ROI (Region of Interest) feature is used to cut out the key feature regions of brain tumor images, and the data set is expanded according to tumor type. The MBConv(Mobile Inverted Bottleneck Convolution) module in EfficientNet is redesigned according to the convolutional network design idea, and the CBAM(Convolutional Block Attention Module) is introduced after the first convolution step. In order to carry out transfer learning more completely, three neurons are attached to the brain tumors without modifying the original output structure. The improved network model has lower complexity and better adapts to the identification of tumor lesions. The transfer learning method is used to fine-tune the public data set figshare-Brain Tumor Dataset. Experimental results show that the improved model achieves a classification accuracy of 99.67% on the public data set, which is about 3.1 percentage points higher than the original EfficientNet-B0 network.

Key words: classification of brain tumors, deep learning, convolutional neural networks, thresholding, class balance, EfficientNet, ECA attention mechanism, CBAM attention mechanism