Research on classification of benign and malignant pulmonary nodules of CT based on residual channel attention module combined with improved ResNet50
LIU Chenqi
YANG Yu
SHI Ting
Abstract：To improve the classification accuracy of benign and malignant pulmonary nodules,we proposed an improved residual network model LNC-Net.Firstly,small convolution kernels were cascaded to instead of large convolution kernels to improve computa-tional efficiency.Secondly,the input backbone and residual module of ResNet50 were reconstructed,and the output part of ResNet50 was replaced by the global average pooling layer to enhance the feature learning ability of the network and reduce the model parameters.Finally,the feature extraction capability of the network was enhanced by fusing the residual channel attention module(RCAM)and the receptive field block-small(RFB-s).Experiments on LIDC dataset showed that the accuracy rate,precision rate,recall rate,F1 score and AUC of the model reached 0.983,0.984,0.987,0.985 and 0.999,respectively.This model can effectively achieve the auxilia-ry diagnosis of benign and malignant pulmonary nodules.
Keywords：Improved residual networkPulmonary nodules classificationModel compressionResidual convolutional attention moduleReceptive field block-small
Publication Date：2025-12-30
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：8( 379-386 )