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An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19

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dc.contributor.authorWoan Ching Serena Lowen_US
dc.contributor.authorJoon Huang Chuahen_US
dc.contributor.authorClarence Augustine T. H. Teeen_US
dc.contributor.authorShazia Anisen_US
dc.contributor.authorMuhammad Ali Shoaiben_US
dc.contributor.authorAmir Faisalen_US
dc.contributor.authorAzira Binti Khalilen_US
dc.contributor.authorKhin Wee Laien_US
dc.date.accessioned2024-05-28T05:51:42Z
dc.date.available2024-05-28T05:51:42Z
dc.date.issued2021
dc.date.submitted2021-10-4
dc.description.abstractPneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant’s technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.en_US
dc.identifier.doi10.1155/2021/5528144
dc.identifier.epage17
dc.identifier.issn1748-670X
dc.identifier.other2310-9
dc.identifier.spage1
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/6693
dc.identifier.volume2021
dc.language.isoenen_US
dc.publisherHindawi Publishingen_US
dc.relation.ispartofComputational and Mathematical Methods in Medicineen_US
dc.titleAn Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19en_US
dc.typeArticleen_US
dspace.entity.typePublication

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