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With the advances in computer\u2010aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID\u201019 pneumonia in computed tomography (CT) scans of the lungs. The complexity and time limitation of the reverse transcription\u2010polymerase chain reaction (RT\u2010PCR) swab test makes it disadvantageous to depend solely on as COVID\u201019\u2019s central diagnostic mechanism. Since CT imaging systems are of low cost and widely available, we demonstrate that the drawback of the RT\u2010PCR can be alleviated with a faster, automated, and reduced contact diagnostic process via the use of a neural network model for the classification of infected and noninfected CT scans. In our proposed model, we explore the benefit of transfer learning as a means of resolving the problem of inadequate dataset and the importance of semisupervised generative adversarial network for the extraction of well\u2010mapped features and generation of image data. Our experimental evaluation indicates that the proposed semisupervised model achieves reliable classification, taking advantage of the reflective loss distance between the real data sample space and the generated data.<\/jats:p>","DOI":"10.1155\/2021\/6680455","type":"journal-article","created":{"date-parts":[[2021,2,17]],"date-time":"2021-02-17T14:02:55Z","timestamp":1613570575000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID\u201019 in CT Images"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9853-9554","authenticated-orcid":false,"given":"Ariyo","family":"Oluwasanmi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9649-7757","authenticated-orcid":false,"given":"Muhammad Umar","family":"Aftab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6745-6377","authenticated-orcid":false,"given":"Zhiguang","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4098-3147","authenticated-orcid":false,"given":"Son Tung","family":"Ngo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5817-4025","authenticated-orcid":false,"given":"Thang Van","family":"Doan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4743-0879","authenticated-orcid":false,"given":"Son Ba","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7809-935X","authenticated-orcid":false,"given":"Son Hoang","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,2,16]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/app10196862"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2020.2993291"},{"key":"e_1_2_9_3_2","article-title":"Computer-aided diagnosis for burnt skin images using deep convolutional neural network","volume":"24","author":"Khan F. 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