Machine learning applications for COVID-19 outbreak management

dc.authoridJafari Navimipour, Nima/0000-0002-5514-5536
dc.authoridHeidari, Arash/0000-0003-4279-8551
dc.authoridToumaj, Shiva/0000-0002-4828-9427
dc.authorwosidJafari Navimipour, Nima/AAF-5662-2021
dc.authorwosidHeidari, Arash/AAK-9761-2021
dc.contributor.authorHeidari, Arash
dc.contributor.authorNavimipour, Nima Jafari
dc.contributor.authorUnal, Mehmet
dc.contributor.authorToumaj, Shiva
dc.date.accessioned2023-10-19T15:12:48Z
dc.date.available2023-10-19T15:12:48Z
dc.date.issued2022
dc.department-temp[Heidari, Arash] Islamic Azad Univ, Dept Comp Engn, Tabriz Branch, Tabriz, Iran; [Heidari, Arash] Islamic Azad Univ, Dept Comp Engn, Shabestar Branch, Shabestar, Iran; [Navimipour, Nima Jafari] Kadir Has Univ, Dept Comp Engn, Istanbul, Turkey; [Unal, Mehmet] Nisantasi Univ, Dept Comp Engn, Istanbul, Turkey; [Toumaj, Shiva] Urmia Univ Med Sci, Orumiyeh, Iranen_US
dc.description.abstractRecently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.en_US
dc.identifier.citation48
dc.identifier.doi10.1007/s00521-022-07424-wen_US
dc.identifier.endpage15348en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue18en_US
dc.identifier.pmid35702664en_US
dc.identifier.scopus2-s2.0-85131558400en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage15313en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-022-07424-w
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5535
dc.identifier.volume34en_US
dc.identifier.wosWOS:000809323500001en_US
dc.identifier.wosqualityQ1
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeepEn_Us
dc.subjectModelEn_Us
dc.subjectImagesEn_Us
dc.subjectMachine learningen_US
dc.subjectApplicationsen_US
dc.subjectDeep
dc.subjectCOVID-19en_US
dc.subjectModel
dc.subjectMedical imagingen_US
dc.subjectImages
dc.subjectOutbreaken_US
dc.titleMachine learning applications for COVID-19 outbreak managementen_US
dc.typeReviewen_US
dspace.entity.typePublication

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