Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Sep:138:109944.
doi: 10.1016/j.chaos.2020.109944. Epub 2020 May 28.

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

Harsh Panwar  1 , P K Gupta  1 , Mohammad Khubeb Siddiqui  2 , Ruben Morales-Menendez  2 , Vaishnavi Singh  1
Affiliations

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

Harsh Panwar et al. Chaos Solitons Fractals. 2020 Sep.

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.

Keywords: COVID-19; Convolutional neural network (CNN); Deep learning; Detection; X-Rays; nCOVnet.

PubMed Disclaimer

Conflict of interest statement

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.

Figures

Fig. 1
Fig. 1
The 2019-nCoV structure. Corona viruses belong in the family Coronaviridae and can cause disease in mammals and birds. The corona virus spike (S) protein mediates membrane fusion by binding to cellular receptors. (reprinted from  with permission under the terms of Creative Commons Attribution 4.0 International License.
Fig. 2
Fig. 2
Sample of the labelled X-rays after data augmentation taken from the combined data set of COVID-19 patients and normal patients.
Fig. 3
Fig. 3
Basic CNN architecture for classification and detection of COVID-19.
Fig. 4
Fig. 4
Model summary of nCOVnet using VGG16 as a base model and five custom layers as head model.
Fig. 5
Fig. 5
Architecture of the VGG16 Model.
Algorithm 1
Algorithm 1
Fast Detection and Classification of COVID-19.
Fig. 6
Fig. 6
ROC curve for nCOVnet.
Fig. 7
Fig. 7
Training curve of loss and Accuracy for nCOVnet models.
Fig. 8
Fig. 8
Prediction results of Covid-19.
See this image and copyright information in PMC

References

    1. Coronaviruses. https://www.niaid.nih.gov/diseases-conditions/coronaviruses, Last accessed on May 2020; 2020.
    1. Fan Y., Zhao K., Shi Z.-L., Zhou P. Bat coronaviruses in China. Viruses. 2019;11(3):210. - PMC - PubMed
    1. Iqbal H.M., Romero-Castillo K.D., Bilal M., Parra-Saldivar R. The emergence of novel-coronavirus and its replication cycle-an overview. J Pure Appl Microbiol. 2020;14(1)
    1. Siddiqui M.K., Morales-Menendez R., Gupta P.K., Iqbal H.M., Hussain F., Khatoon K. Correlation between temperature and covid-19 (suspected, confirmed and death) cases based on machine learning analysis. J Pure Appl Microbiol. 2020;14:1017–1024. doi: 10.22207/JPAM.14.SPL1.40. 6201. - DOI
    1. Bilal M., Nazir M., Parra-Saldivar R., Iqbal H.M. 2019-ncov/covid-19 - approaches to viral vaccine development and preventive measures. J Pure Appl Microbiol. 2020;14(1)

LinkOut - more resources