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

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dc.authoridKavuran, Gurkan/0000-0003-2651-5005;
dc.authorwosidKavuran, Gurkan/S-6935-2016
dc.authorwosidŞAHİN, MAHMUT/HKW-3047-2023
dc.contributor.authorKavuran, Gurkan
dc.contributor.authorIn, Erdal
dc.contributor.authorGeckil, Aysegul Altintop
dc.contributor.authorSahin, Mahmut
dc.contributor.authorBerber, Nurcan Kirici
dc.date.accessioned2024-08-04T21:00:01Z
dc.date.available2024-08-04T21:00:01Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_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.doi10.1016/j.clinimag.2021.09.007
dc.identifier.endpage8en_US
dc.identifier.issn0899-7071
dc.identifier.issn1873-4499
dc.identifier.pmid34592696en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.clinimag.2021.09.007
dc.identifier.urihttps://hdl.handle.net/11616/103720
dc.identifier.volume81en_US
dc.identifier.wosWOS:000705378600001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Science Incen_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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