基于CSNet网络的肺炎图像分类研究OA

X-ray images are an important imaging basis for the diagnosis of pneumonia disease.Due to the diversity of lung diseases,the accuracy of pneumonia diagnosis needs to be further improved.Based on the ConvNeXt network model,a new convolutional neural network model CSNet is proposed for four classification of X-ray pneumonia images.Based on the ConvNeXt network model,the CSNet network changed the original image preprocessing part and pooling layer,and added a feature channel attention module to the convolution block to highlight the pneumonia information in the feature map.On the basis of the attention module,the activation function was modified and compared,and finally the SMU activation function was selected.The effectiveness of each module was proved by ablation experiments,and the effectiveness of the network was verified by comparative experiments with 7 networks.Experimental results show that compared with other network models,the proposed model has the highest accuracy of 99.0%,and the precision and recall are higher.