Double Branch Pneumonia Image Classification Network Based on Attention Mechanism
At present,most pneumonia image classification networks use single branch network to extract features from input images,which to some extent ignores the feature information of different dimensions of images.In order to optimize this problem,this paper adopts double branch network with VGG16 network and convolutional neural network added separable convolution and CBAM for feature extraction respectively.The two networks can pay attention to feature information of pneumonia images at different dimensions.Then,the features of the two networks are fused and input into the full connection layer for classification decision.Experiments show that the network achieves 95%accuracy in the test set of normal lung,viral pneumonia and COVID-19 X-ray images.The ablation experiments prove that the feature fusion module and attention module added to the network play a significant role in reducing network parameters and improving the accuracy of network classification.The result by comparing the performance with other networks also shows that this network has higher accuracy and stronger robustness in pneumonia image classification.
COVID-19pneumonia image classificationattention mechanismdouble branch network feature extraction and fusion
万方数据
维普
