ARTIFICIAL INTELLIGENCE-ASSISTED PREDICTION OF COVID-19 STATUS BASED ON THORAX CT SCANS USING A PROPOSED META-LEARNING STRATEGY

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dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridSevgi, Serkan/0000-0001-6995-0256
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.contributor.authorGuldogan, Emek
dc.contributor.authorYilderim, Ismail Okan
dc.contributor.authorSevgi, Serkan
dc.contributor.authorColak, Cemil
dc.date.accessioned2024-08-04T20:52:04Z
dc.date.available2024-08-04T20:52:04Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground: Radiological techniques integrated with artificial intelligence (AI) are a promising diagnostic tool for the rapidly increasing number of COVID-19 cases today. In this study, we intended to construct an artificial intelligence-assisted prediction of COVID-19 status based on thorax computed tomography (CT) scans using a proposed meta-learning strategy. Methods: A public dataset including 1252 positive and 1230 negative thorax CT scans of SARS-CoV-2 was used in the current study. The CT images for COVID-19 status were analyzed by 26 transfer learning (TL) models. The stacking ensemble learning was used to obtain more consistent and high-performance prediction results by combining the prediction results of 26 TL models with an Results: Mobile had the best prediction with an accuracy of 0.946 (95% CI: 0.93-0.962) among the TL models. The Meta-learning model yielded the best classification accuracy of 0.993 (0.98-1), which outperformed MobileNet, the most successful architecture Conclusions: The proposed meta-model that can distinguish CT images between COVID-19 positive and abnormal/normal conditions due to other etiology of COVID-19 negative may be beneficial in such pandemics. The AI application in this study can be used in mobile, desktop, and web-based platforms to have facilitating and complementary effects on classical reporting and the current workload in radiology departments.en_US
dc.description.sponsorshipInonu University Scientific Research Projects Coordination Unit [TOA-2020-2204]en_US
dc.description.sponsorshipThis study was supported by Inonu University Scientific Research Projects Coordination Unit within the scope of TOA-2020-2204 numbered research project.en_US
dc.identifier.doi10.19193/0393-6384_2022_3_228
dc.identifier.endpage1521en_US
dc.identifier.issn0393-6384
dc.identifier.issn2283-9720
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85132445912en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1515en_US
dc.identifier.urihttps://doi.org/10.19193/0393-6384_2022_3_228
dc.identifier.urihttps://hdl.handle.net/11616/100732
dc.identifier.volume38en_US
dc.identifier.wosWOS:000820966400014en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherCarbone Editoreen_US
dc.relation.ispartofActa Medica Mediterraneaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputed tomographyen_US
dc.subjectCOVID-19en_US
dc.subjecttransfer learning modelsen_US
dc.subjectmeta-learningen_US
dc.subjectXGBoosten_US
dc.titleARTIFICIAL INTELLIGENCE-ASSISTED PREDICTION OF COVID-19 STATUS BASED ON THORAX CT SCANS USING A PROPOSED META-LEARNING STRATEGYen_US
dc.typeArticleen_US

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