An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

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dc.authoridKAYA, Yasin/0000-0002-9074-0189
dc.authoridGursoy, Ercan/0000-0001-6974-2705
dc.contributor.authorGursoy, Ercan
dc.contributor.authorKaya, Yasin
dc.date.accessioned2025-01-06T17:37:15Z
dc.date.available2025-01-06T17:37:15Z
dc.date.issued2023
dc.description.abstractThe World Health Organization (WHO) declared a pandemic in response to the coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the disease appears to have lost its impact, millions of people have been affected by this virus, and new infections still occur. Identifying COVID-19 requires a reverse transcription-polymerase chain reaction test (RT-PCR) or analysis of medical data. Due to the high cost and time required to scan and analyze medical data, researchers are focusing on using automated computer-aided methods. This review examines the applications of deep learning (DL) and machine learning (ML) in detecting COVID-19 using medical data such as CT scans, X-rays, cough sounds, MRIs, ultrasound, and clinical markers. First, the data preprocessing, the features used, and the current COVID-19 detection methods are divided into two subsections, and the studies are discussed. Second, the reported publicly available datasets, their characteristics, and the potential comparison materials mentioned in the literature are presented. Third, a comprehensive comparison is made by contrasting the similar and different aspects of the studies. Finally, the results, gaps, and limitations are summarized to stimulate the improvement of COVID-19 detection methods, and the study concludes by listing some future research directions for COVID-19 classification.
dc.identifier.doi10.1007/s00530-023-01083-0
dc.identifier.endpage1627
dc.identifier.issn0942-4962
dc.identifier.issn1432-1882
dc.identifier.issue3
dc.identifier.pmid37261262
dc.identifier.scopus2-s2.0-85150746651
dc.identifier.scopusqualityQ1
dc.identifier.startpage1603
dc.identifier.urihttps://doi.org/10.1007/s00530-023-01083-0
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2163
dc.identifier.volume29
dc.identifier.wosWOS:000956512200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofMultimedia Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectCOVID-19
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectTransfer learning
dc.subjectX-ray
dc.subjectCT scan
dc.subjectCNN models
dc.titleAn overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works
dc.typeArticle

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