Deep learning model for the automatic classification of COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy: a multi-center retrospective study.
Deep learning model for the automatic classification of COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy: a multi-center retrospective study.
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DOI：
10.1038/s41598-022-11990-3
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发表时间：
2022-05-17
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
--
作者：
这项回顾性研究旨在开发和验证用于冠状病毒病分类的深度学习模型-2019年新冠肺炎肺炎、非新冠肺炎肺炎和健康人使用胸部X光图像。其中包括一个CXR图像的私有数据集和两个公共数据集。私人数据集包括来自六家医院的CXR。共有14,258张和11,253张CXR图像包含在两个公共数据集中，455张包含在私人数据集中。利用这三个数据集构建了一个基于EfficientNet的噪声学生深度学习模型。通过深度学习模型和6位放射科医生对私人数据集中150张CXR图像的测试集进行了评估。计算新冠肺炎肺炎、非新冠肺炎肺炎和健康者的三类分类准确率和分类曲线下面积(AUC)。六位放射科医生的共识被用于计算班级AUC。我们模型的三类分类准确率为0.8667，六位放射科医生的分类准确率在0.5667到0.7733之间。对于我们的模型和六位放射科医生的共识，健康、非新冠肺炎肺炎和新冠肺炎肺炎的分级AUC值分别为0.9912、0.9492和0.9752和0.9656、0.8654和0.8740。对于新冠肺炎肺炎，我们的模型与六位放射科医生的共识之间的分级AUC差异有统计学意义(p值 = ，0.001334)。因此，可以为三类分类构建一个准确的深度学习模型；我们的模型的诊断性能明显好于六位放射科医生对新冠肺炎肺炎的共识解释。
This retrospective study aimed to develop and validate a deep learning model for the classification of coronavirus disease-2019 (COVID-19) pneumonia, non-COVID-19 pneumonia, and the healthy using chest X-ray (CXR) images. One private and two public datasets of CXR images were included. The private dataset included CXR from six hospitals. A total of 14,258 and 11,253 CXR images were included in the 2 public datasets and 455 in the private dataset. A deep learning model based on EfficientNet with noisy student was constructed using the three datasets. The test set of 150 CXR images in the private dataset were evaluated by the deep learning model and six radiologists. Three-category classification accuracy and class-wise area under the curve (AUC) for each of the COVID-19 pneumonia, non-COVID-19 pneumonia, and healthy were calculated. Consensus of the six radiologists was used for calculating class-wise AUC. The three-category classification accuracy of our model was 0.8667, and those of the six radiologists ranged from 0.5667 to 0.7733. For our model and the consensus of the six radiologists, the class-wise AUC of the healthy, non-COVID-19 pneumonia, and COVID-19 pneumonia were 0.9912, 0.9492, and 0.9752 and 0.9656, 0.8654, and 0.8740, respectively. Difference of the class-wise AUC between our model and the consensus of the six radiologists was statistically significant for COVID-19 pneumonia (p value = 0.001334). Thus, an accurate model of deep learning for the three-category classification could be constructed; the diagnostic performance of our model was significantly better than that of the consensus interpretation by the six radiologists for COVID-19 pneumonia.
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影响因子：
19.5
作者：
Selvaraju, Ramprasaath R.;Cogswell, Michael;Batra, Dhruv
通讯作者：
Batra, Dhruv
影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S
影响因子：
8
作者：
Shorfuzzaman M;Hossain MS
通讯作者：
Hossain MS
影响因子：
7.6
作者：
Sharma A;Rani S;Gupta D
通讯作者：
Gupta D
DOI：
10.1109/access.2021.3058537
发表时间：
2021
期刊：
IEEE access : practical innovations, open solutions
影响因子：
--
作者：
Islam MM;Karray F;Alhajj R;Zeng J
通讯作者：
Zeng J