{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T06:17:00Z","timestamp":1792736220602,"version":"4.5.0"},"reference-count":32,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T00:00:00Z","timestamp":1628640000000},"content-version":"vor","delay-in-days":222,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007446","name":"King Khalid University","doi-asserted-by":"publisher","award":["4-N-20\/21"],"award-info":[{"award-number":["4-N-20\/21"]}],"id":[{"id":"10.13039\/501100007446","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The COVID\u201019 pandemic has had a significant impact on public life and health worldwide, putting the world\u2019s healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak\u2019s spread, and restore full functionality to the world\u2019s healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID\u201019. However, chest X\u2010ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID\u201019 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep\u2010 and transfer\u2010learning techniques to develop accurate, general, and robust models for detecting COVID\u201019. The developed models utilize either convolutional neural networks or transfer\u2010learning models or hybridize them with powerful machine\u2010learning techniques to exploit their full potential. For experimentation, we applied the proposed models to two data sets: the COVID\u201019 Radiography Database from Kaggle and a local data set from Asir Hospital, Abha, Saudi Arabia. The proposed models achieved promising results in detecting COVID\u201019 cases and discriminating them from normal and other viral pneumonia with excellent accuracy. The hybrid models extracted features from the flatten layer or the first hidden layer of the neural network and then fed these features into a classification algorithm. This approach enhanced the results further to full accuracy for binary COVID\u201019 classification and 97.8% for multiclass classification.<\/jats:p>","DOI":"10.1155\/2021\/9996737","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T21:50:11Z","timestamp":1628718611000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Hybrid Deep\u2010Learning and Machine\u2010Learning Models for Predicting COVID\u201019"],"prefix":"10.1155","volume":"2021","author":[{"given":"Talal S.","family":"Qaid","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1665-5947","authenticated-orcid":false,"given":"Hussein","family":"Mazaar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Yahya H.","family":"Al-Shamri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed S.","family":"Alqahtani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abeer A.","family":"Raweh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wafaa","family":"Alakwaa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,8,11]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107613"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bcp.2020.114184"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00444-8"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2018170048"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1177\/2472630320958376"},{"key":"e_1_2_11_7_2","doi-asserted-by":"crossref","unstructured":"GaoT. Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN 2020 https:\/\/www.medrxiv.org\/content\/10.1101\/2020.08.20.20178913v2.","DOI":"10.21203\/rs.3.rs-64537\/v2"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2020200097"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01867-1"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05410-8"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0247839"},{"key":"e_1_2_11_12_2","unstructured":"CohenJ. P. MorrisonP. andLanD. COVID-19 image data collection 2020 https:\/\/arxiv.org\/abs\/2003.11597."},{"key":"e_1_2_11_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/42.476112"},{"key":"e_1_2_11_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-020-00865-4"},{"key":"e_1_2_11_15_2","unstructured":"RajpurkarP. IrvinJ. ZhuK.et al. Chexnet: radiologist-level pneumonia detection on chest X-Rays with deep learning 2017 https:\/\/arxiv.org\/abs\/1711.05225."},{"key":"e_1_2_11_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104181"},{"key":"e_1_2_11_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.110713"},{"key":"e_1_2_11_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110495"},{"key":"e_1_2_11_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100505"},{"key":"e_1_2_11_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/v12070769"},{"key":"e_1_2_11_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101794"},{"key":"e_1_2_11_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01902-1"},{"key":"e_1_2_11_23_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-020-00831-x"},{"key":"e_1_2_11_24_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-76550-z"},{"key":"e_1_2_11_25_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0242535"},{"key":"e_1_2_11_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100360"},{"key":"e_1_2_11_27_2","doi-asserted-by":"crossref","unstructured":"KumarR. AroraR. BansalV.et al. Prediction of COVID-19 using chest x-ray images through deep feature learning model with SMOTE and machine learning classifiers 2020 https:\/\/www.medrxiv.org\/content\/10.1101\/2020.04.13.20063461v1.","DOI":"10.1101\/2020.04.13.20063461"},{"key":"e_1_2_11_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2788044"},{"key":"e_1_2_11_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102589"},{"key":"e_1_2_11_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3004555"},{"key":"e_1_2_11_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2871027"},{"key":"e_1_2_11_32_2","unstructured":"AhmedS. Abd El-SamieF. El-LatifA. andMohamedH. Combined COVID-19 dataset\u201d Mendeley data 2020 https:\/\/doi.org\/10.17632\/3pxjb8knp7.3."}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/9996737.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/9996737.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/9996737","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T12:17:14Z","timestamp":1722946634000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/9996737"}},"subtitle":[],"editor":[{"given":"Cornelio","family":"Y\u00e1\u00f1ez-M\u00e1rquez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/9996737"],"URL":"https:\/\/doi.org\/10.1155\/2021\/9996737","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-03-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-22","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"9996737"}}