Online diagnosis of COVID-19 from chest radiography images by using deep learning algorithms

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dc.authorid0000-0002-8470-4579en_US
dc.authorid0000-0002-3769-0071en_US
dc.contributor.authorBudak, Cafer
dc.contributor.authorMençik, Vasfiye
dc.contributor.authorVarışlı, Osman
dc.date.accessioned2023-08-16T06:20:11Z
dc.date.available2023-08-16T06:20:11Z
dc.date.issued2023en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThe COVID-19 outbreak, which has a devastating impact on the health and well-being of the global population, is a respiratory disease. It is vital to determine, isolate and treat people with the disease as soon as possible to fight against the COVID-19 pandemic. Even though the reverse transcription polymerase chain reaction (RT-PCR) test, the accuracy of which is about 63%, seems to be a good option for determining COVID-19, it is a disadvantage is that test kits are few, are difficult to obtain in remote rural areas and have low accuracy. Chest X-ray (CXR) has become essential for rapidly diagnosing the rapidly spreading COVID-19 disease worldwide, so it is urgent to develop an online system that will help specialists identify infected patients with CXR images. In this study developed a transfer learning-based diagnosis system for online diagnosis of COVID-19 patients using CXR images. Transfer learning-based deep learning models VGG16, VGG19, ResNet50, InceptionV3, Xception, MobileNet, DenseNet121 and DenseNet201 were used for the experimental studies. We explored the COVID-19 radiography database from Kaggle, which is open to the public, using image preprocessing techniques and data augmentation. The images captured by the various terminals are transferred to the web server in the created system. Similar to the ensemble learning approach, the percentage accuracy of the model with the highest prediction value among the eight deep learning models is displayed on the screen. The results show that the proposed online diagnosis system performs better than others with the highest accuracy, precision, recall and F1 values of 98%, 99%, 97% and 97%, respectively. The results show that deep learning models help to increase the efficiency of chest radiograph scanning and have promising potential in predicting COVID-19 cases. The online diagnostic system will be a helpful tool for radiologists as it diagnoses COVID-19 quickly and with high accuracy.en_US
dc.identifier.citationBudak, C., Mençik, V. ve Varışlı, O. (2023). Online diagnosis of COVID-19 from chest radiography images by using deep learning algorithms. Neural Computing and Applications, 1-18.en_US
dc.identifier.doi10.1007/s00521-023-08867-5
dc.identifier.endpage18en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.scopus2-s2.0-85165588608
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://link.springer.com/article/10.1007/s00521-023-08867-5
dc.identifier.urihttps://hdl.handle.net/11468/12493
dc.identifier.wosWOS:001034484100004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBudak, Cafer
dc.institutionauthorMençik, Vasfiye
dc.institutionauthorVarışlı, Osman
dc.language.isoenen_US
dc.publisherSpringer Science and Business Mediaen_US
dc.relation.ispartofNeural Computing and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectCOVID-19en_US
dc.subjectOnline detectionen_US
dc.subjectFine-tuning strategyen_US
dc.titleOnline diagnosis of COVID-19 from chest radiography images by using deep learning algorithmsen_US
dc.titleOnline diagnosis of COVID-19 from chest radiography images by using deep learning algorithms
dc.typeArticleen_US

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