基于CT影像的COVID-19智能辅助诊断方法

目的结合COVID-19患者肺部CT影像学特征，探讨深度学习技术在COVID-19辅助诊断上的价值。方法搜集武汉大学中南医院和华中科技大学同济医学院确诊为COVID-19患者的部分CT影像资料构建小样本COVID-19数据集，将VGG-16具有提取高层抽象特征部分与设计的全连接层共同构成初步的基于迁移学习的COVID-19智能辅助诊断模型，使用COVID-19训练集迭代训练诊断模型，不断优化全连接层网络参数，最后训练出一个基于VGG-16卷积神经网络迁移学习的COVID-19智能辅助诊断模型。结果在COVID-19测试集中早期、进展期和重症期3个类别的样本上，COVID-19智能辅助诊断模型测试的敏感度分别为0.95、0.93和0.96，F1 Score分别为0.98、0.95和0.92，综合的诊断准确率达到94.59%。结论小样本数据集上采用迁移学习技术训练的COVID-19辅助诊断模型具有较高的可靠性，在防控疫情的关键时期，能快速地为医生提供诊断的参考意见，提高医生的工作效率。

Intelligent assisted diagnosis of COVID-19 based on CT images

ObjectiveTo investigate the value of deep learning technology in the diagnosis of pneumonitis associated with new coronavirus infection in combination with CT imaging features of patients with new coronavirus infected pneumonia (COVID- 19).MethodsWe collected CT image data of patients diagnosed as COVID-19 in Central South Hospital of Wuhan University and Tongji Medical College of Huazhong University of Science and Technology to construct a small sample COVID-19 data set. VGG-16 was taken to extract high- level abstract features and design the full The connection layer together constitutes the preliminary COVID-19 intelligent auxiliary diagnosis model based on transfer learning. The COVID-19 training set was used to iteratively train the diagnosis model, continuously optimize the parameters of the fully connected layer network. We finally trained a migration based on the VGG-16 convolutional neural network. Learning COVID-19 intelligent assistant diagnosis model.ResultsOn the samples of the three categories of early, advanced and severe stages in the COVID-19 test set, the sensitivities of the COVID-19 intelligent auxiliary diagnostic model test were 0.95, 0.93 and 0.96, and the F1 Scores were 0.98, 0.95 and 0.92, respectively. The comprehensive diagnostic accuracy rate reached 94.59%.ConclusionThe COVID-19 assisted diagnosis model trained with transfer learning technology on a small sample data set has high reliability. It can provide doctors with reference opinions for diagnosis and improve doctors' work efficiency in the critical period of epidemic prevention and control.