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dc.contributor.authorUçar, Murat
dc.date.accessioned2022-11-22T08:25:07Z
dc.date.available2022-11-22T08:25:07Z
dc.date.issued2022en_US
dc.identifier.citationUçar, M. (2022). Automatic segmentation of COVID-19 from computed tomography images using modified U-Net model-based majority voting approach. Neural Computing and Applications, 34 (24), pp. 21927-21938. https://doi.org/10.1007/s00521-022-07653-zen_US
dc.identifier.urihttps://doi.org/10.1007/s00521-022-07653-z
dc.identifier.urihttps://hdl.handle.net/20.500.12508/2305
dc.description.abstractThe coronavirus disease (COVID-19) is an important public health problem that has spread rapidly around the world and has caused the death of millions of people. Therefore, studies to determine the factors affecting the disease, to perform preventive actions and to find an effective treatment are at the forefront. In this study, a deep learning and segmentation-based approach is proposed for the detection of COVID-19 disease from computed tomography images. The proposed model was created by modifying the encoder part of the U-Net segmentation model. In the encoder part, VGG16, ResNet101, DenseNet121, InceptionV3 and EfficientNetB5 deep learning models were used, respectively. Then, the results obtained with each modified U-Net model were combined with the majority vote principle and a final result was reached. As a result of the experimental tests, the proposed model obtained 85.03% Dice score, 89.13% sensitivity and 99.38% specificity on the COVID-19 segmentation test dataset. The results obtained in the study show that the proposed model will especially benefit clinicians in terms of time and cost.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.isversionof10.1007/s00521-022-07653-zen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCOVID-19en_US
dc.subjectDeep learningen_US
dc.subjectMajority votingen_US
dc.subjectSegmentationen_US
dc.subject.classificationRadiological Findings
dc.subject.classificationClinical Features
dc.subject.classificationCOVID-19
dc.subject.classificationComputer Science
dc.subject.classificationElectrical Engineering, Electronics & Computer Science - Computer Vision & Graphics - Retinal Images
dc.subject.otherComputerized tomography
dc.subject.otherDeep learning
dc.subject.otherImage segmentation
dc.subject.otherLearning systems
dc.subject.otherSignal encoding
dc.subject.otherStatistical tests
dc.subject.otherAutomatic segmentations
dc.subject.otherComputed tomography images
dc.subject.otherCoronaviruses
dc.subject.otherDeep learning
dc.subject.otherMajority voting
dc.subject.otherModel-based OPC
dc.subject.otherNet model
dc.subject.otherPreventive action
dc.subject.otherSegmentation
dc.subject.otherVoting approach
dc.subject.otherCOVID-19
dc.titleAutomatic segmentation of COVID-19 from computed tomography images using modified U-Net model-based majority voting approachen_US
dc.typearticleen_US
dc.relation.journalNeural Computing and Applicationsen_US
dc.contributor.departmentİşletme ve Yönetim Bilimleri Fakültesi -- Yönetim Bilişim Sistemleri Bölümüen_US
dc.identifier.volume34en_US
dc.identifier.issue24en_US
dc.identifier.startpage21927en_US
dc.identifier.endpage21938en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.isteauthorUçar, Murat
dc.relation.indexWeb of Science - Scopus - PubMeden_US
dc.relation.indexWeb of Science Core Collection - Science Citation Index Expanded


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