MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net

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dc.contributor.authorKavuran, Gürkan
dc.contributor.authorİn, Erdal
dc.contributor.authorAltıntop Geçkil, Ayşegül
dc.contributor.authorŞahin, Mahmut
dc.contributor.authorKırıcı Berber, Nurcan
dc.date.accessioned2022-04-25T06:04:26Z
dc.date.available2022-04-25T06:04:26Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümüen_US
dc.description.abstractPurpose The aim of this study was to establish and evaluate a fully automatic deep learning system for the diagnosis of COVID-19 using thoracic computed tomography (CT). Materials and methods In this retrospective study, a novel hybrid model (MTU-COVNet) was developed to extract visual features from volumetric thoracic CT scans for the detection of COVID-19. The collected dataset consisted of 3210 CT scans from 953 patients. Of the total 3210 scans in the final dataset, 1327 (41%) were obtained from the COVID-19 group, 929 (29%) from the CAP group, and 954 (30%) from the Normal CT group. Diagnostic performance was assessed with the area under the receiver operating characteristic (ROC) curve, sensitivity, and specificity. Results The proposed approach with the optimized features from concatenated layers reached an overall accuracy of 97.7% for the CT-MTU dataset. The rest of the total performance metrics, such as; specificity, sensitivity, precision, F1 score, and Matthew Correlation Coefficient were 98.8%, 97.6%, 97.8%, 97.7%, and 96.5%, respectively. This model showed high diagnostic performance in detecting COVID-19 pneumonia (specificity: 98.0% and sensitivity: 98.2%) and CAP (specificity: 99.1% and sensitivity: 97.1%). The areas under the ROC curves for COVID-19 and CAP were 0.997 and 0.996, respectively. Conclusion A deep learning–based AI system built on the CT imaging can detect COVID-19 pneumonia with high diagnostic efficiency and distinguish it from CAP and normal CT. AI applications can have beneficial effects in the fight against COVID-19.en_US
dc.identifier.citationKavuran, G., In, E., Geçkil, A. A., Şahin, M., & Berber, N. K. (2022). MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net. Clinical Imaging, 81, 1-8.en_US
dc.identifier.doi10.1016/j.clinimag.2021.09.007
dc.identifier.endpage8en_US
dc.identifier.pmid34592696
dc.identifier.scopus2-s2.0-85115886761en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.clinimag.2021.09.007
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1029
dc.identifier.volume81en_US
dc.identifier.wosWOS:000705378600001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorKavuran, Gürkan
dc.institutionauthorİn, Erdal
dc.institutionauthorAltıntop Geçkil, Ayşegül
dc.institutionauthorKırıcı Berber, Nurcan
dc.language.isoenen_US
dc.publisherelsevieren_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCOVID-19en_US
dc.subjectPneumoniaen_US
dc.subjectArtificial intelligence (AI)en_US
dc.subjectDeep learningen_US
dc.subjectComputed tomography (CT)en_US
dc.titleMTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-neten_US
dc.typeArticleen_US

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