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

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dc.authorid0000-0003-2651-5005en_US
dc.authorid0000-0002-8807-5853en_US
dc.authorid0000-0003-0348-3194en_US
dc.authorid0000-0003-0348-3194
dc.contributor.authorKavuran, Gürkan
dc.contributor.authorİn, Erdal
dc.contributor.authorAltıntop Geçkil, Ayşegül
dc.contributor.authorŞahin, Mahmut
dc.contributor.authorKırıcı Berber, Nurcan
dc.date.accessioned2021-10-18T06:18:20Z
dc.date.available2021-10-18T06:18:20Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.departmentMTÖ Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümüen_US
dc.description.abstractCOVID-19PneumoniaArtificial intelligence (AI)Deep learningComputed tomography (CT)en_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., İn, 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.issn0899-7071en_US
dc.identifier.pmid34592696
dc.identifier.scopus2-s2.0-85115886761en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.clinimag.2021.09.007
dc.identifier.uri1873-4499
dc.identifier.urihttps://hdl.handle.net/20.500.12899/452
dc.identifier.volume81en_US
dc.identifier.wosWOS:000705378600001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_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.ispartofClinical Imagingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_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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