{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T04:28:20Z","timestamp":1771820900907,"version":"3.50.1"},"reference-count":14,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T00:00:00Z","timestamp":1597708800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Ministry of Education of Malaysia","doi-asserted-by":"crossref","award":["FRGS19-181-0790"],"award-info":[{"award-number":["FRGS19-181-0790"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["International Journal of Biomedical Imaging"],"published-print":{"date-parts":[[2020,8,18]]},"abstract":"<jats:p>The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.<\/jats:p>","DOI":"10.1155\/2020\/8828855","type":"journal-article","created":{"date-parts":[[2020,8,19]],"date-time":"2020-08-19T00:17:52Z","timestamp":1597796272000},"page":"1-7","source":"Crossref","is-referenced-by-count":104,"title":["COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5496-0822","authenticated-orcid":true,"given":"Mohd Zulfaezal","family":"Che Azemin","sequence":"first","affiliation":[{"name":"Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Bandar Indera Mahkota, 25200 Kuantan, Pahang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2144-8416","authenticated-orcid":true,"given":"Radhiana","family":"Hassan","sequence":"additional","affiliation":[{"name":"Kulliyyah of Medicine, International Islamic University Malaysia, Bandar Indera Mahkota, 25200 Kuantan, Pahang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1397-8174","authenticated-orcid":true,"given":"Mohd Izzuddin","family":"Mohd Tamrin","sequence":"additional","affiliation":[{"name":"Kulliyyah of ICT, International Islamic University Malaysia, 50728 Gombak, Kuala Lumpur, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7203-0610","authenticated-orcid":true,"given":"Mohd Adli","family":"Md Ali","sequence":"additional","affiliation":[{"name":"Kulliyyah of Science, International Islamic University Malaysia, Bandar Indera Mahkota, 25200 Kuantan, Pahang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-06801-0"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/ciaa199"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1002\/ppul.24718"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_30"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91008-6_63"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-42294-8"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019191293"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.16.16963"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01228-7"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1148\/rg.312105714"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1201\/b15964-5"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002686"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103795"}],"container-title":["International Journal of Biomedical Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijbi\/2020\/8828855.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijbi\/2020\/8828855.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijbi\/2020\/8828855.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,19]],"date-time":"2020-08-19T00:18:00Z","timestamp":1597796280000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/ijbi\/2020\/8828855\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,18]]},"references-count":14,"alternative-id":["8828855","8828855"],"URL":"https:\/\/doi.org\/10.1155\/2020\/8828855","relation":{},"ISSN":["1687-4188","1687-4196"],"issn-type":[{"value":"1687-4188","type":"print"},{"value":"1687-4196","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,18]]}}}