Scopus:
Automated detection of Covid-19 disease using deep fused features from chest radiography images

dc.contributor.authorUçar E.
dc.contributor.authorAtila Ü.
dc.contributor.authorUçar M.
dc.contributor.authorAkyol K.
dc.date.accessioned2023-04-12T00:45:21Z
dc.date.available2023-04-12T00:45:21Z
dc.date.issued2021-08-01
dc.description.abstractThe health systems of many countries are desperate in the face of Covid-19, which has become a pandemic worldwide and caused the death of hundreds of thousands of people. In order to keep Covid-19, which has a very high propagation rate, under control, it is necessary to develop faster, low-cost and highly accurate methods, rather than a costly Polymerase Chain Reaction test that can yield results in a few hours. In this study, a deep learning-based approach that can detect Covid-19 quickly and with high accuracy on X-ray images, which are common in every hospital and can be obtained at low cost, was proposed. Deep features were extracted from X-Ray images in RGB, CIE Lab and RGB CIE color spaces using DenseNet121 and EfficientNet B0 pre-trained deep learning architectures and then obtained features were fed into a two-stage classifier approach. Each of the classifiers in the proposed approach performed binary classification. In the first stage, healthy and infected samples were separated, and in the second stage, infected samples were detected as Covid-19 or pneumonia. In the experiments, Bi-LSTM network and well-known ensemble approaches such as Gradient Boosting, Random Forest and Extreme Gradient Boosting were used as the classifier model and it was seen that the Bi-LSTM network had a superior performance than other classifiers with 92.489% accuracy.
dc.identifier.doi10.1016/j.bspc.2021.102862
dc.identifier.issn17468094
dc.identifier.scopus2-s2.0-85108086040
dc.identifier.urihttps://hdl.handle.net/20.500.12597/4396
dc.relation.ispartofBiomedical Signal Processing and Control
dc.rightsfalse
dc.subjectAutomatic medical diagnosis | Bi-LSTM | Covid-19 | Deep learning | Pneumonia | X-ray
dc.titleAutomated detection of Covid-19 disease using deep fused features from chest radiography images
dc.typeArticle
dspace.entity.typeScopus
oaire.citation.volume69
person.affiliation.nameIskenderun Technical University
person.affiliation.nameGazi Üniversitesi
person.affiliation.nameIskenderun Technical University
person.affiliation.nameKastamonu University
person.identifier.scopus-author-id57219782288
person.identifier.scopus-author-id37074174900
person.identifier.scopus-author-id57219780113
person.identifier.scopus-author-id57188723065
relation.isPublicationOfScopus3a53be11-b5a7-4055-af31-d601627e8951
relation.isPublicationOfScopus.latestForDiscovery3a53be11-b5a7-4055-af31-d601627e8951

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