Automated COVID-19 detection in chest X-ray images usingfine-tuned deep learning architectures

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dc.authorid0000-0003-2078-2145en_US
dc.authorid0000-0002-7201-6963en_US
dc.authorid0000-0003-2078-2145en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorAggarwal, Sonam
dc.contributor.authorGupta, Sheifali
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorKoundal, Deepika
dc.contributor.authorGupta, Rupesh
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-06-20T08:09:39Z
dc.date.available2023-06-20T08:09:39Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis publication was supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia.en_US
dc.description.abstractThe COVID-19 pandemic has a significant impact on human health globally. The illness is due to the presence of a virus manifesting itself in a widespread disease resulting in a high mortality rate in the whole world. According to the study, infected patients have distinct radiographic visual characteristics as well as dry cough, breathlessness, fever, and other symptoms. Although, the reverse transcription polymerase-chain reaction (RT-PCR) test has been used for COVID-19 testing its reliability is very low. Therefore, computed tomography and X-ray images have been widely used. Artificial intelligence coupled with X-ray technologies has recently shown to be more effective in the diagnosis of this disease. With this motivation, a comparative analysis of fine-tuned deep learning architectures has been made to speed up the detection and classification of COVID-19 patients from other pneumonia groups. The models used for this analysis are MobileNetV2, ResNet50, InceptionV3, NASNetMobile, VGG16, Xception, InceptionResNetV2 DenseNet121, which have been fine-tuned using a new set of layers replaced with the head of the network. This research work has carried out an analysis on two datasets. Dataset-1 includes the images of three classes: Normal, COVID, and Pneumonia. Dataset-2, in contrast, contains the same classes with more focus on two prominent pneumonia categories: bacterial pneumonia and viral pneumonia. The research was conducted on 959 X-ray images (250 of Bacterial Pneumonia, 250 of Viral Pneumonia, 209 of COVID, and 250 of Normal cases). Using the confusion matrix, the required results of different models have been computed. For the first dataset, DenseNet121 has obtained a 97% accuracy, while for the second dataset, MobileNetV2 has performed best with an accuracy of 81%.en_US
dc.description.sponsorshipPrince Sattam bin Abdulaziz University, Alkharj, Saudi Arabiaen_US
dc.identifier.citationAggarwal, S., Gupta, S., Alhudhaif, A., Koundal, D., Gupta, R., & Polat, K. (2022). Automated COVID‐19 detection in chest X‐ray images using fine‐tuned deep learning architectures. Expert Systems, 39(3), e12749.en_US
dc.identifier.doi10.1111/exsy.12749
dc.identifier.endpage17en_US
dc.identifier.issn0266-4720
dc.identifier.issn1468-0394
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85107403096en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1111/exsy.12749
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11148
dc.identifier.volume39en_US
dc.identifier.wosWOS:000659733100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofExpert Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectChest X-Raysen_US
dc.subjectCOVID-19en_US
dc.subjectDeep Learningen_US
dc.subjectTransfer Learningen_US
dc.titleAutomated COVID-19 detection in chest X-ray images usingfine-tuned deep learning architecturesen_US
dc.typeArticleen_US

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