基于轻量级RG-DenseNet的COVID-19 CT图像分类  

COVID-19 classification on CT image using lightweight RG DenseNet

作　　者：张子宇 赵可辉[2] 牛慧芳 张志强[1] 周连田 ZHANG Ziyu;ZHAO Kehui;NIU Huifang;ZHANG Zhiqiang;ZHOU Liantian(College of Intelligence and Information Engineering,Shandong University of Traditional Chinese Medicine,Jinan 250000,China;Special Inspection Department,the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine,Jinan 250000,China;Shandong Province Adverse Drug Reaction Testing Center,Jinan 250000,China;Department of Lithotripsy,Heze Traditional Chinese Medicine Hospital,Heze 247000,China)

机构地区：[1]山东中医药大学智能与信息工程学院,山东济南250000 [2]山东中医药大学第二附属医院特检科,山东济南250000 [3]山东省药品不良反应检测中心,山东济南250000 [4]菏泽市中医医院碎石科,山东菏泽247000

出　　处：《中国医学物理学杂志》2023年第12期1494-1501,共8页Chinese Journal of Medical Physics

基　　金：中国药品监管科学研究行动计划第二批重点项目(2022SDADRKY06)。

摘　　要：目的:基于轻量级RG-DenseNet构建COVID-19 CT图像分类模型。方法:以DenseNet121为基础,添加通道和空间注意力机制模块减少无关特征的干扰,将DenseNet中的Bottleneck模块替换为前激活的RG-beneck2模块减少模型参数的同时保持精度尽可能不变。构建RG-DenseNet模型,在COVIDx CT-2A数据集上进行3分类实验。结果:RG-DenseNet准确率为98.93%、精确率为98.70%、召回率为98.97%、特异性为99.48%、F1分数为98.83%。结论:RG-DenseNet与原模型DenseNet121相比在保持准确度仅降低0.01%的情况下,减少92.7%的参数量和计算量,轻量化效果显著,具有实际应用价值。Objective To construct a COVID-19 CT image classification model based on lightweight RG DenseNet.Methods A RG-DenseNet model was constructed by adding channel and spatial attention modules to DenseNet121 for minimizing the interference of irrelevant features,and replacing Bottleneck module in DenseNet with pre-activated RG beneck2 module for reducing model parameters while maintaining accuracy as much as possible.The model performance was verified with 3-category classification experiments on the COVIDx CT-2A dataset.Results RG-DenseNet had an accuracy,precision,recall rate,specificity,and F1-score of 98.93%,98.70%,98.97%,99.48%,and 98.83%,respectively.Conclusion Compared with the original model DenseNet121,RG-DenseNet reduces the number of parameters and the computational complexity by 92.7%,while maintaining an accuracy reduction of only 0.01%,demonstrating a significant lightweight effect and high practical application value.

关 键 词：RepGhost DenseNet COVID-19 深度学习 图像分类 

分 类 号：R318[医药卫生—生物医学工程] TP391[医药卫生—基础医学]