COVID-19 Prediction from Chest X-Ray Images using Transfer Learning

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dc.contributor.authorBıçakcı, Kaan
dc.contributor.authorTunalı, Volkan
dc.date.accessioned2023-04-10T20:27:56Z
dc.date.available2023-04-10T20:27:56Z
dc.date.issued2021
dc.departmentRektörlük, Rektörlüğe Bağlı Birimler, Düzce Üniversitesi Dergilerien_US
dc.description.abstractThe COVID-19 pandemic has been affecting our lives in many ways, not only the healthcare systems in the countries but the whole societies worldwide. Meantime, a considerable number of studies have been conducted and lots of medical techniques have been tried to overcome the pandemic. In this work, making use of real-world images, we applied Convolutional Neural Networks to chest X-ray images to predict whether a patient has the COVID-19 virus or not. Initially, we used transfer learning to fine tune a number of pre-trained ResNet, VGG, and Xception models, which are very well-known architectures due to their success in image processing tasks. While the achieved performance with these models was encouraging, we ensembled three models to obtain more accurate and reliable results. Finally, our ensemble model outperformed all other models with an F-Score of 97%.en_US
dc.identifier.doi10.29130/dubited.878779
dc.identifier.endpage1407en_US
dc.identifier.issn2148-2446
dc.identifier.issue4en_US
dc.identifier.startpage1395en_US
dc.identifier.trdizinid498561en_US
dc.identifier.urihttp://doi.org/10.29130/dubited.878779
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/498561
dc.identifier.urihttps://hdl.handle.net/20.500.12684/11814
dc.identifier.volume9en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofDüzce Üniversitesi Bilim ve Teknoloji Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleCOVID-19 Prediction from Chest X-Ray Images using Transfer Learningen_US
dc.typeArticleen_US

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