Classification of Benign and Malignant Pulmonary Nodules Based on Dual Channel Parallel Network
In view of the lack of feature extraction ability in traditional classification methods for benign and malignant pul-monary nodules,a dual-channel parallel network model combining residual network and Swin-Transformer is proposed,and the accuracy of benign and malignant pulmonary nodules classification is effectively improved by adding a triple attention module. The network improves the classification accuracy with the help of the original CT image and the contour image of pulmonary nodules. Adding triple attention to connect the two networks is beneficial to explore the benign and malignant characteristics of pulmonary nodules. The proposed method was verified on the LIDC-IDRI dataset,and the AUC reached 0.9517,with an accurate rate reached 0.9311. The experimental results show that the classification accuracy of ResNet-Swin Transformer is higher than that of ResNet and Swin Transformer. The diagnostic rate of pulmonary nodules can be improved.
benign and malignant classificationResNetSwin-Transformerattention mechanism
NETL
NSTL
万方数据
