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The training dataset contained 26,477, 2468, and 8104 CT images of normal, CAP, and COVID-19, respectively. The validation dataset contained 14,076, 1028, and 3376 CT images of normal, CAP, and COVID-19 patients, respectively. The test set included 51 normal cases, 28 CAP patients, and 51 COVID-19 patients. We designed and trained a deep learning model to recognize normal, CAP, and COVID-19 patients based on U-Net and ResNet-50. Moreover, the diagnoses of the deep learning model were compared with different levels of radiologists.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the test set, the sensitivity of the deep learning model in diagnosing normal cases, CAP, and COVID-19 patients was 98.03%, 89.28%, and 92.15%, respectively. The diagnostic accuracy of the deep learning model was 93.84%. In the validation set, the accuracy was 92.86%, which was better than that of two novice doctors (86.73% and 87.75%) and almost equal to that of two experts (94.90% and 93.88%). The AI model performed\u00a0significantly\u00a0better\u00a0than all four radiologists in terms of time consumption (35\u00a0min vs. 75\u00a0min, 93\u00a0min, 79\u00a0min, and 82\u00a0min).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The AI model we obtained had strong decision-making ability, which could potentially assist doctors in detecting COVID-19 pneumonia.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-022-02022-1","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T17:20:03Z","timestamp":1667409603000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A novel deep learning-based method for COVID-19 pneumonia detection from CT images"],"prefix":"10.1186","volume":"22","author":[{"given":"Ju","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingshu","family":"Chi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Canxia","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"2022_CR1","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/S0140-6736(20)30211-7","volume":"395","author":"N Chen","year":"2020","unstructured":"Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. 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All patient and image information was erased by the IEEE Signal Processing Society. According to national legislation and institutional requirements, informed consent was waived by the Ethics Committee of the Third Xiangya Hospital of Central South University due to the public datasets and retrospective nature of this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"284"}}