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Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.
Panwar, Harsh; Gupta, P K; Siddiqui, Mohammad Khubeb; Morales-Menendez, Ruben; Singh, Vaishnavi.
  • Panwar H; Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.
  • Gupta PK; Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.
  • Siddiqui MK; School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL, Mexico.
  • Morales-Menendez R; School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL, Mexico.
  • Singh V; Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.
Chaos Solitons Fractals ; 138: 109944, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-401363
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study Language: English Journal: Chaos Solitons Fractals Year: 2020 Document Type: Article Affiliation country: J.chaos.2020.109944

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study Language: English Journal: Chaos Solitons Fractals Year: 2020 Document Type: Article Affiliation country: J.chaos.2020.109944