Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.
Identifieur interne : 002623 ( Main/Exploration ); précédent : 002622; suivant : 002624Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.
Auteurs : Harsh Panwar [Inde] ; P K Gupta [Inde] ; Mohammad Khubeb Siddiqui [Mexique] ; Ruben Morales-Menendez [Mexique] ; Vaishnavi Singh [Inde]Source :
- Chaos, solitons, and fractals [ 0960-0779 ] ; 2020.
Abstract
Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.
DOI: 10.1016/j.chaos.2020.109944
PubMed: 32536759
PubMed Central: PMC7254021
Affiliations:
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Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234</wicri:regionArea><wicri:noRegion>173 234</wicri:noRegion></affiliation></author><author><name sortKey="Gupta, P K" sort="Gupta, P K" uniqKey="Gupta P" first="P K" last="Gupta">P K Gupta</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234</wicri:regionArea><wicri:noRegion>173 234</wicri:noRegion></affiliation></author><author><name sortKey="Siddiqui, Mohammad Khubeb" sort="Siddiqui, Mohammad Khubeb" uniqKey="Siddiqui M" first="Mohammad Khubeb" last="Siddiqui">Mohammad Khubeb Siddiqui</name><affiliation wicri:level="1"><nlm:affiliation>School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL, Mexico.</nlm:affiliation><country xml:lang="fr">Mexique</country><wicri:regionArea>School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL</wicri:regionArea><wicri:noRegion>NL</wicri:noRegion></affiliation></author><author><name sortKey="Morales Menendez, Ruben" sort="Morales Menendez, Ruben" uniqKey="Morales Menendez R" first="Ruben" last="Morales-Menendez">Ruben Morales-Menendez</name><affiliation wicri:level="1"><nlm:affiliation>School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL, Mexico.</nlm:affiliation><country xml:lang="fr">Mexique</country><wicri:regionArea>School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL</wicri:regionArea><wicri:noRegion>NL</wicri:noRegion></affiliation></author><author><name sortKey="Singh, Vaishnavi" sort="Singh, Vaishnavi" uniqKey="Singh V" first="Vaishnavi" last="Singh">Vaishnavi Singh</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234</wicri:regionArea><wicri:noRegion>173 234</wicri:noRegion></affiliation></author></analytic><series><title level="j">Chaos, solitons, and fractals</title><idno type="ISSN">0960-0779</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several <i>radiology specialists</i> and <i>time</i> to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32536759</PMID><DateRevised><Year>2020</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0960-0779</ISSN><JournalIssue CitedMedium="Print"><Volume>138</Volume><PubDate><Year>2020</Year><Month>Sep</Month></PubDate></JournalIssue><Title>Chaos, solitons, and fractals</Title><ISOAbbreviation>Chaos Solitons Fractals</ISOAbbreviation></Journal><ArticleTitle>Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.</ArticleTitle><Pagination><MedlinePgn>109944</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.chaos.2020.109944</ELocationID><Abstract><AbstractText>Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several <i>radiology specialists</i> and <i>time</i> to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.</AbstractText><CopyrightInformation>© 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Panwar</LastName><ForeName>Harsh</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gupta</LastName><ForeName>P K</ForeName><Initials>PK</Initials><AffiliationInfo><Affiliation>Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Siddiqui</LastName><ForeName>Mohammad Khubeb</ForeName><Initials>MK</Initials><AffiliationInfo><Affiliation>School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL, Mexico.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Morales-Menendez</LastName><ForeName>Ruben</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL, Mexico.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Singh</LastName><ForeName>Vaishnavi</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>05</Month><Day>28</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Chaos Solitons Fractals</MedlineTA><NlmUniqueID>100971564</NlmUniqueID><ISSNLinking>0960-0779</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Convolutional neural network (CNN)</Keyword><Keyword MajorTopicYN="N">Deep learning</Keyword><Keyword MajorTopicYN="N">Detection</Keyword><Keyword MajorTopicYN="N">X-Rays</Keyword><Keyword MajorTopicYN="N">nCOVnet</Keyword></KeywordList><CoiStatement>We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. No funding was received for this work. We further confirm that any aspect of the work covered in this manuscript that has involved human patients has been conducted with the ethical approval of all relevant bodies and that such approvals are acknowledged within the manuscript.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>05</Month><Day>10</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>05</Month><Day>26</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>6</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>6</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>6</Month><Day>17</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32536759</ArticleId><ArticleId IdType="doi">10.1016/j.chaos.2020.109944</ArticleId><ArticleId IdType="pii">S0960-0779(20)30343-X</ArticleId><ArticleId IdType="pii">109944</ArticleId><ArticleId IdType="pmc">PMC7254021</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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