COVID-19 detection using chest X-ray images based on a developed deep neural network

dc.authoridMousavi, Zohreh/0000-0001-6866-3095
dc.authorscopusid57214259904
dc.authorscopusid57386177100
dc.authorscopusid57207883911
dc.authorscopusid57219257313
dc.authorscopusid57448533800
dc.authorwosidMousavi, Zohreh/GNM-5848-2022
dc.contributor.authorMousavi, Zohreh
dc.contributor.authorShahini, Nahal
dc.contributor.authorSheykhivand, Sobhan
dc.contributor.authorMojtahedi, Sina
dc.contributor.authorArshadi, Afrooz
dc.date.accessioned2024-05-25T11:27:40Z
dc.date.available2024-05-25T11:27:40Z
dc.date.issued2022
dc.departmentOkan Universityen_US
dc.department-temp[Mousavi, Zohreh] Univ Tabriz, Fac Mech Engn, Dept Mech Engn, Tabriz, Iran; [Shahini, Nahal] Amirkabir Univ Technol, Dept Comp Engn & Informat Technol, Tehran, Iran; [Sheykhivand, Sobhan] Univ Tabriz, Fac Elect & Comp Engn, Dept Biomed Engn, Tabriz, Iran; [Mojtahedi, Sina] Okan Univ, Fac Engn, Dept Elect & Elect Engn, Istanbul, Turkey; [Arshadi, Afrooz] Univ Allameh Tabatabai, Fac Math Sci & Comp, Dept Stat, Tehran, Iranen_US
dc.descriptionMousavi, Zohreh/0000-0001-6866-3095en_US
dc.description.abstractAim: Currently, a new coronavirus called COVID-19 is the biggest challenge of the human at 21st century. Now, the spread of this virus is such that mortality has risen strongly in all cities of countries. Therefore, it is necessary to think of a solution to handle the disease by fast and timely diagnosis. This paper proposes a method that uses chest X-ray imagery to divide 2-4 classes into 7 different Scenarios, including Bacterial, Viral, Healthy, and COVID-19 classes. The aim of this study is to propose a method that uses chest X-ray imagery to divide 2-4 classes into 7 different Scenarios, including Bacterial, Viral, Healthy, and COVID-19 classes. Methods: 6 different databases from chest X-ray imagery that have been widely used in recent studies have been gathered for this aim. A Convolutional Neural Network-Long Short Time Memory model is designed and developed to extract features from raw data hierarchically. In order to make more realistic assumptions and use the Proposed Method in the practical field, white Gaussian noise is added to the raw chest X-ray imagery. Additionally, the proposed network is tested and investigated not only on 6 expressed databases but also on two additional databases. Results: On the test set, the proposed network achieved an accuracy of more than 90% for all Scenarios excluding Scenario V, i.e. Healthy against the COVID-19 against the Viral, and also achieved 99% accuracy for separating the COVID-19 from the Healthy group. The results showed that the proposed network is robust to noise up to 1 dB. It is worth noting that the proposed network for two additional databases, which were only used as test databases, also achieved more than 90% accuracy. In addition, in comparison to the state-of-the-art pneumonia detection approaches, the final results obtained from the proposed network is so promising. Conclusions: The proposed network is effective in detecting COVID-19 and other lung infectious diseases using chest X-ray imagery and can thus assist radiologists in making rapid and accurate detections.en_US
dc.identifier.citation27
dc.identifier.doi10.1016/j.slast.2021.10.011
dc.identifier.endpage75en_US
dc.identifier.issn2472-6303
dc.identifier.issn2472-6311
dc.identifier.issue1en_US
dc.identifier.pmid35058196
dc.identifier.scopus2-s2.0-85124436685
dc.identifier.scopusqualityQ2
dc.identifier.startpage63en_US
dc.identifier.urihttps://doi.org/10.1016/j.slast.2021.10.011
dc.identifier.urihttps://hdl.handle.net/20.500.14517/1068
dc.identifier.volume27en_US
dc.identifier.wosWOS:000780677300009
dc.identifier.wosqualityQ3
dc.language.isoen
dc.publisherElsevier Science incen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject[No Keyword Available]en_US
dc.titleCOVID-19 detection using chest X-ray images based on a developed deep neural networken_US
dc.typeArticleen_US
dspace.entity.typePublication

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