信号处理2025，Vol.41Issue(2)：253-266,14.DOI:10.12466/xhcl.2025.02.005
多分支特征融合分类网络用于CXR图像识别
Multi-Branch Feature Fusion Classification Network for Chest X-Ray Image Recognition
摘要
Abstract
COVID-19 is an infectious disease caused by the new coronavirus,which poses a significant challenge to global public health.In clinical practice,chest X-ray(CXR)examinations are an important means by which to identify COVID-19 infections and other common lung diseases.However,it is time-consuming and labor-intensive for radiolo-gists to examine COVID-19 patients,and such procedures increase the risk of infection for doctors.Therefore,an algo-rithm that can automatically identify COVID-19 from chest X-ray images is particularly important.Therefore,this paper proposes a CXR image classification framework based on deep learning that can generate more discriminative features with limited training data.Specifically,a multi-branch classification network is first formed by residual neural networks(ResNet34 and ResNet50)and a Transformer.The ResNet branch effectively extracts rich semantic information and deli-cate texture information through a deep residual structure,whereas the Transformer branch captures the global semantic features of the image through a self-attention mechanism.Then,the feature interaction module is used to extract rich se-mantic and texture information from the ResNet branch,and the feature interaction is performed with the global seman-tic features extracted by the Transformer.Finally,the multiscale semantic features of the image are extracted through the feature fusion module.This method can extract multiscale feature representations under the condition of limited training data to extract features and locate COVID-19 infected areas.The experiment was compared with 15 methods on the pub-lic DLAI3 and COVIDx data sets,and the accuracy was improved by 1.37% and 0.76%,respectively,compared with the ResNet50 model.The classification method proposed in this paper combines the advantages of ResNet and Trans-former networks in feature extraction to make the recognition results of the network more accurate for CXR images.关键词
胸部X射线检查/特征交互模块/多分支分类网络/残差神经网络/TransformerKey words
chest X-ray/feature interaction module/multi-branch classification network/residual neural network/Transformer分类
信息技术与安全科学引用本文复制引用
苏华强,雷海军,雷柏英..多分支特征融合分类网络用于CXR图像识别[J].信号处理,2025,41(2):253-266,14.基金项目
国家自然科学基金(62276172) （62276172）
广东省自然科学基金(2023A1515011378) （2023A1515011378）
深圳市基础研究专项(JCYJ20230808105602005)The National Natural Science Foundation of China(62276172) （JCYJ20230808105602005）
Natural Science Foundation of Guangdong Province(2023A1515011378) （2023A1515011378）
Shenzhen Basic Research Project(JCYJ20230808105602005) （JCYJ20230808105602005）
