Research on Chest CT Image Classification Method Combining Attention Mechanism and Lightweight Convolutional Neural Network
CT images of the same disease type can also show differences due to the different severity of the patient's disease.At present,main clinical diagnosis methods rely on personal ability and past experience of doctors,and the objectivity needs to be enhanced and the efficiency needs to be improved.In view of these problems,we proposed a CT classification network with attention mechanism-parallel lightweight convolutional neural network for CT classification(PC-CTNet).This network mainly consisted of parallel branch channel shuffle(PCS)module and deep-wise efficient shortcut connection(DES)module.PCS module adopted double branches,fused the features under the multi-scale receptive field.DES module used convolution and efficient channel attention to extract effective deep inter-class differentiation information,and alleviated gradient disappearance by shortcut connection.Experiments were conducted on two chest CT datasets,and the results showed that the classification accuracy of the PC-CTNet model reached 98.46％on the collected dataset with 5988 CT images in different sizes,and 98.75％on the open-source datasets with 194 922 CT images.The performance indicators of PC-CTNet were close to the existing chest CT classification network,and its parameter and computational complexity was about 0.32 M and 75.58 M,respectively,which was 10.17％and 3.21％of the chest CT classification network in the experimental comparison.The proposed network has higher parameter and computational efficiency,can effectively assist doctors in diagnosis and improve diagnostic efficiency and objectivity.
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