Automatic detection and localization of COVID-19 pneumonia using axial computed tomography images and deep convolutional neural networks

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dc.authorid0000-0002-9368-8902en_US
dc.authorid0000-0002-2435-7800en_US
dc.contributor.authorPolat, Hasan
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.contributor.authorEkici, Faysal
dc.contributor.authorAkpolat, Veysi
dc.date.accessioned2021-06-01T08:11:34Z
dc.date.available2021-06-01T08:11:34Z
dc.date.issued2021en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionWOS:000618279300001
dc.descriptionPMID: 33821092
dc.description.abstractCOVID-19 was first reported as an unknown group of pneumonia in Wuhan City, Hubei province of China in late December of 2019. The rapid increase in the number of cases diagnosed with COVID-19 and the lack of experienced radiologists can cause diagnostic errors in the interpretation of the images along with the exceptional workload occurring in this process. Therefore, the urgent development of automated diagnostic systems that can scan radiological images quickly and accurately is important in combating the pandemic. With this motivation, a deep convolutional neural network (CNN)-based model that can automatically detect patterns related to lesions caused by COVID-19 from chest computed tomography (CT) images is proposed in this study. In this context, the image ground-truth regarding the COVID-19 lesions scanned by the radiologist was evaluated as the main criteria of the segmentation process. A total of 16 040 CT image segments were obtained by applying segmentation to the raw 102 CT images. Then, 10 420 CT image segments related to healthy lung regions were labeled as COVID-negative, and 5620 CT image segments, in which the findings related to the lesions were detected in various forms, were labeled as COVID-positive. With the proposed CNN architecture, 93.26% diagnostic accuracy performance was achieved. The sensitivity and specificity performance metrics for the proposed automatic diagnosis model were 93.27% and 93.24%, respectively. Additionally, it has been shown that by scanning the small regions of the lungs, COVID-19 pneumonia can be localized automatically with high resolution and the lesion densities can be successfully evaluated quantitatively.en_US
dc.identifier.citationPolat, H., Özerdem, M.S., Ekici, F. ve Akpolat, V. (2021). Automatic detection and localization of COVID-19 pneumonia using axial computed tomography images and deep convolutional neural networks. International Journal of Imaging Systems and Technology, 31(2), 509-524.en_US
dc.identifier.doi10.1002/ima.22558
dc.identifier.endpage524en_US
dc.identifier.issn0899-9457
dc.identifier.issn1098-1098
dc.identifier.issue2en_US
dc.identifier.pmid33821092
dc.identifier.scopus2-s2.0-85101429340
dc.identifier.scopusqualityQ1
dc.identifier.startpage509en_US
dc.identifier.urihttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ima.22558
dc.identifier.urihttps://hdl.handle.net/11468/7003
dc.identifier.volume31en_US
dc.identifier.wosWOS:000618279300001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorÖzerdem, Mehmet Siraç
dc.institutionauthorEkici, Faysal
dc.institutionauthorAkpolat, Veysi
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofInternational Journal of Imaging Systems and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClassificationen_US
dc.subjectComputer-aided diagnosisen_US
dc.subjectConvolutional neural networksen_US
dc.subjectcoronavirusen_US
dc.subjectCOVID19en_US
dc.subjectDeep learningen_US
dc.subjectRadiologyen_US
dc.titleAutomatic detection and localization of COVID-19 pneumonia using axial computed tomography images and deep convolutional neural networksen_US
dc.titleAutomatic detection and localization of COVID-19 pneumonia using axial computed tomography images and deep convolutional neural networks
dc.typeArticleen_US

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