dc.contributor.author | Alsharif, Mohammed H. | |
dc.contributor.author | Alsharif, Yahia H. | |
dc.contributor.author | Yahya, Khalid O. Moh. | |
dc.contributor.author | Alomari, Osama Ahmad | |
dc.contributor.author | Albreem, Mahmoud A. M. | |
dc.contributor.author | Jahid, Abu | |
dc.date.accessioned | 2023-08-18T10:39:24Z | |
dc.date.available | 2023-08-18T10:39:24Z | |
dc.date.issued | 2020 | en_US |
dc.identifier.issn | 1128-3602 | |
dc.identifier.uri | https://hdl.handle.net/11363/5373 | |
dc.description.abstract | Recent Coronavirus (COVID-19)
is one of the respiratory diseases, and it is
known as fast infectious ability. This dissemination can be decelerated by diagnosing and
quarantining patients with COVID-19 at early
stages, thereby saving numerous lives. Reverse
transcription-polymerase chain reaction (RTPCR) is known as one of the primary diagnostic tools. However, RT-PCR tests are costly and
time-consuming; it also requires specific materials, equipment, and instruments. Moreover,
most countries are suffering from a lack of testing kits because of limitations on budget and
techniques. Thus, this standard method is not
suitable to meet the requirements of fast detection and tracking during the COVID-19 pandemic, which motived to employ deep learning (DL)/
convolutional neural networks (CNNs) technology with X-ray and CT scans for efficient analysis and diagnostic. This study provides insight
about the literature that discussed the deep
learning technology and its various techniques
that are recently developed to combat the dissemination of COVID-19 disease. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | VERDUCI PUBLISHER, VIA GREGORIO VII, ROME 186-00165, ITALY | en_US |
dc.relation.isversionof | 10.26355/eurrev_202011_23640 | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Artificial intelligence | en_US |
dc.subject | Coronavirus pandemic | en_US |
dc.subject | AI | en_US |
dc.subject | SARS-CoV-2 | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Big data | en_US |
dc.subject | COVID-19 | en_US |
dc.title | Deep learning applications to combat the dissemination of COVID-19 disease: a review | en_US |
dc.type | article | en_US |
dc.relation.ispartof | European Review for Medical and Pharmacological Sciences | en_US |
dc.department | Mühendislik ve Mimarlık Fakültesi | en_US |
dc.authorid | https://orcid.org/0000-0001-8579-5444 | en_US |
dc.authorid | https://orcid.org/0000-0002-0792-7031 | en_US |
dc.authorid | https://orcid.org/0000-0002-6464-1101 | en_US |
dc.identifier.volume | 24 | en_US |
dc.identifier.issue | 21 | en_US |
dc.identifier.startpage | 11455 | en_US |
dc.identifier.endpage | 11460 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.institutionauthor | Yahya, Khalid O. Moh. | |
dc.institutionauthor | Alomari, Osama Ahmad | |