Detection of novel coronavirus from chest X-rays using deep convolutional neural networks.

Content Provider	Europe PMC
Author	Sanket, Shashwat Vergin Raja Sarobin, M. Jani Anbarasi, L. Thakor, Jayraj Singh, Urmila Narayanan, Sathiya
Abstract	With over 172 Million people infected with the novel coronavirus (COVID-19) globally and with the numbers increasing exponentially, the dire need of a fast diagnostic system keeps on surging. With shortage of kits, and deadly underlying disease due to its vastly mutating and contagious properties, the tired physicians need a fast diagnostic method to cater the requirements of the soaring number of infected patients. Laboratory testing has turned out to be an arduous, cost-ineffective and requiring a well-equipped laboratory for analysis. This paper proposes a convolutional neural network (CNN) based model for analysis/detection of COVID-19, dubbed as CovCNN, which uses the patient’s chest X-ray images for the diagnosis of COVID-19 with an aim to assist the medical practitioners to expedite the diagnostic process amongst high workload conditions. In the proposed CovCNN model, a novel deep-CNN based architecture has been incorporated with multiple folds of CNN. These models utilize depth wise convolution with varying dilation rates for efficiently extracting diversified features from chest X-rays. 657 chest X-rays of which 219 were X-ray images of patients infected from COVID-19 and the remaining were the images of non-COVID-19 (i.e. normal or COVID-19 negative) patients. Further, performance evaluation on the dataset using different pre-trained models has been analyzed based on the loss and accuracy curve. The experimental results show that the highest classification accuracy (98.4%) is achieved using the proposed CovCNN model.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC8423603&blobtype=pdf
ISSN	13807501
Journal	Multimedia Tools and Applications [Multimed Tools Appl]
Volume Number	81
DOI	10.1007/s11042-021-11257-5
PubMed Central reference number	PMC8423603
Issue Number	16
PubMed reference number	34512112
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2021-09-08
Publisher Place	New York
Access Restriction	Open
Rights License	This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
Subject Keyword	Convolutional neural network Deep-CNN COVID-19 detection X-rays
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology