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The manual inspection of the latter, besides being slow, is susceptible to human errors, especially because of an uncanny resemblance between the CT scans of COVID-19 and those of pneumonia, and therefore demands a proportional increase in the number of expert radiologists. Artificial intelligence-based computer-aided diagnosis of COVID-19 using the CT scans has been recently coined, which has proven its effectiveness in terms of accuracy and computation time. In this work, a similar framework for classification of COVID-19 using CT scans is proposed. The proposed method includes four core steps: (i) preparing a database of three different classes such as COVID-19, pneumonia, and normal; (ii) modifying three pretrained deep learning models such as VGG16, ResNet50, and ResNet101 for the classification of COVID-19-positive scans; (iii) proposing an activation function and improving the firefly algorithm for feature selection; and (iv) fusing optimal selected features using descending order serial approach and classifying using multiclass supervised learning algorithms. We demonstrate that once this method is performed on a publicly available dataset, this system attains an improved accuracy of 97.9% and the computational time is almost 34 (sec).<\/jats:p>","DOI":"10.1155\/2021\/2560388","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T20:20:39Z","timestamp":1640636439000},"page":"1-13","source":"Crossref","is-referenced-by-count":27,"title":["A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images"],"prefix":"10.1155","volume":"2021","author":[{"given":"Hassaan Haider","family":"Syed","sequence":"first","affiliation":[{"name":"Department of Computer Science, HITEC University Taxila, Museum Road, Taxila, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7058-0715","authenticated-orcid":true,"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer 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