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.
关键词
新冠肺炎/肺炎图像分类/注意力机制/双分支特征提取和融合
Key words
COVID-19/pneumonia image classification/attention mechanism/double branch network feature extraction and fusion
维普
万方数据