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dc.contributor.authorRasheed, Jawad
dc.contributor.authorHameed, Alaa Ali
dc.contributor.authorDjeddi, Chawki
dc.contributor.authorJamil, Akhtar
dc.contributor.authorAl-Turjman, Fadi
dc.date.accessioned2022-03-04T19:12:13Z
dc.date.available2022-03-04T19:12:13Z
dc.date.issued2021
dc.identifier.issn1913-2751
dc.identifier.issn1867-1462
dc.identifier.urihttps://doi.org/10.1007/s12539-020-00403-6
dc.identifier.urihttps://hdl.handle.net/20.500.12436/3126
dc.description.abstractCorona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers were selected: logistic regression (LR) and convolutional neural networks (CNN). The main reason was to make the system fast and efficient. Moreover, a dimensionality reduction approach was also investigated based on principal component analysis (PCA) to further speed up the learning process and improve the classification accuracy by selecting the highly discriminate features. The deep learning-based methods demand large amount of training samples compared to conventional approaches, yet adequate amount of labelled training samples was not available for COVID-19 X-ray images. Therefore, data augmentation technique using generative adversarial network (GAN) was employed to further increase the training samples and reduce the overfitting problem. We used the online available dataset and incorporated GAN to have 500 X-ray images in total for this study. Both CNN and LR showed encouraging results for COVID-19 patient identification. The LR and CNN models showed 95.2-97.6% overall accuracy without PCA and 97.6-100% with PCA for positive cases identification, respectively.en_US
dc.language.isoengen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofInterdisciplinary Sciences-Computational Life Sciencesen_US
dc.identifier.doi10.1007/s12539-020-00403-6
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectComputer-aided diagnosisen_US
dc.subjectCOVID-19en_US
dc.subjectImage classificationen_US
dc.subjectPrincipal component analysisen_US
dc.subjectCONVOLUTIONAL NEURAL-NETWORKSen_US
dc.titleA machine learning-based framework for diagnosis of COVID-19 from chest X-ray imagesen_US
dc.typearticleen_US
dc.authoridAl-Turjman, Fadi/0000-0001-5418-873X
dc.authoridRasheed, Jawad/0000-0003-3761-1641
dc.authoridDjeddi, Chawki/0000-0002-8436-827X
dc.authoridJAMIL, AKHTAR/0000-0002-2592-1039
dc.departmentİZÜen_US
dc.identifier.volume13en_US
dc.identifier.issue1en_US
dc.identifier.startpage103en_US
dc.identifier.endpage117en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.department-temp[Rasheed, Jawad; Hameed, Alaa Ali; Jamil, Akhtar; Al-Turjman, Fadi] Istanbul Sabahattin Zaim Univ, Dept Comp Engn, TR-34303 Istanbul, Turkey; [Djeddi, Chawki] Larbi Tebessi Univ, Dept Math & Comp Sci, Tebessa 12018, Algeria; Near East Univ, Res Ctr AI & IoT, Artificial Intelligence Dept, Mersin 10, Nicosia, Turkeyen_US
dc.authorwosidAl-Turjman, Fadi/L-2998-2019
dc.authorwosidRasheed, Jawad/AAY-5207-2020
dc.authorwosidJAMIL, AKHTAR/M-6215-2019
dc.authorwosidDjeddi, Chawki/AAZ-9378-2021
dc.authorscopusid57205069065
dc.authorscopusid56338374100
dc.authorscopusid55078188200
dc.authorscopusid49863650600
dc.authorscopusid20336944100
dc.identifier.wosWOS:000604143900001en_US
dc.identifier.scopus2-s2.0-85098793591en_US
dc.identifier.pmid33387306en_US


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