Canayaz, MuratSehribanoglu, SanemOzdag, RecepDemir, Murat2025-05-102025-05-1020220941-06431433-305810.1007/s00521-022-07052-42-s2.0-85125416894https://doi.org/10.1007/s00521-022-07052-4https://hdl.handle.net/20.500.14720/14384Canayaz, Murat/0000-0001-8120-5101; Ozdag, Recep/0000-0001-5247-5591; Demir, Murat/0000-0001-7362-0401; Sehribanoglu, Sanem/0000-0002-3099-7599Early diagnosis of COVID-19, the new coronavirus disease, is considered important for the treatment and control of this disease. The diagnosis of COVID-19 is based on two basic approaches of laboratory and chest radiography, and there has been a significant increase in studies performed in recent months by using chest computed tomography (CT) scans and artificial intelligence techniques. Classification of patient CT scans results in a serious loss of radiology professionals' valuable time. Considering the rapid increase in COVID-19 infections, in order to automate the analysis of CT scans and minimize this loss of time, in this paper a new method is proposed using BO (BO)-based MobilNetv2, ResNet-50 models, SVM and kNN machine learning algorithms. In this method, an accuracy of 99.37% was achieved with an average precision of 99.38%, 99.36% recall and 99.37% F-score on datasets containing COVID and non-COVID classes. When we examine the performance results of the proposed method, it is predicted that it can be used as a decision support mechanism with high classification success for the diagnosis of COVID-19 with CT scans.eninfo:eu-repo/semantics/openAccessCoronavirusChest Computed TomographyKnnSvmBayesian OptimizationArticle347Q2Q15349536535250180WOS:000762199300001