Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. (September 2020)

• Record Type: Journal Article
• Authors: Panwar, Harsh; Gupta, P.K.; Siddiqui, Mohammad Khubeb; Morales-Menendez, Ruben; Singh, Vaishnavi
• Is Part Of: Chaos, solitons and fractals. Volume 138(2020)
• Publication Date: 2020-09
• Subjects: COVID-19 -- Detection -- X-Rays -- Deep learning -- Convolutional neural network (CNN) -- nCOVnet; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2020.109944
• Languages: English
• ISSNs: 0960-0779

Abstract

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.