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. 2021 Feb 16;11:3938. doi: 10.1038/s41598-021-83237-6

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Performance of ML models and radiologists on the internal testing dataset. (a) ROC and precision-recall (PR) curve analyses were performed for DL-ML models. The performance of radiologists was dotted according to their sensitivity and specificity. (b) Confusion matrices for binary classification of COVID-19 and other community-acquired pneumonia (CAP). The exact number of true positives, false positives, true negatives, and false negatives were listed. (c) ROC and PR curve analyses on independent internal test data. A batch of etiologically confirmed influenza and mycoplasma pneumonia data was utilized in the internal testing dataset. DL-MLP displayed an adequate performance in distinguishing COVID-19 from them.