生物医学工程研究2025，Vol.44Issue(6)：379-386,8.DOI:10.19529/j.cnki.1672-6278.2025.06.05
残差通道注意力模块结合改进ResNet50的CT肺结节良恶性分类研究
Research on classification of benign and malignant pulmonary nodules of CT based on residual channel attention module combined with improved ResNet50
刘晨琪 1杨愉 1石婷2
作者信息
- 1. 上海理工大学 健康科学与工程学院,上海 200093
- 2. 上海静安区中心医院,上海 200040
- 折叠
摘要
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.关键词
改进残差网络/肺结节分类/模型压缩/残差通道注意力模块/感受野模块Key words
Improved residual network/Pulmonary nodules classification/Model compression/Residual convolutional attention module/Receptive field block-small分类
医药卫生引用本文复制引用
刘晨琪,杨愉,石婷..残差通道注意力模块结合改进ResNet50的CT肺结节良恶性分类研究[J].生物医学工程研究,2025,44(6):379-386,8.
