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The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias Elgendi, Mohamed; Nasir, Muhammad Umer; Tang, Qunfeng; Fletcher, Richard Ribon; Howard, Newton; Menon, Carlo; Ward, Rabab Kreidieh; Nicolaou, Savvas

Abstract

Chest radiography is a critical tool in the early detection, management planning, and follow-up evaluation of COVID-19 pneumonia; however, in smaller clinics around the world, there is a shortage of radiologists to analyze large number of examinations especially performed during a pandemic. Limited availability of high-resolution computed tomography and real-time polymerase chain reaction in developing countries and regions of high patient turnover also emphasizes the importance of chest radiography as both a screening and diagnostic tool. In this paper, we compare the performance of 17 available deep learning algorithms to help identify imaging features of COVID19 pneumonia. We utilize an existing diagnostic technology (chest radiography) and preexisting neural networks (DarkNet-19) to detect imaging features of COVID-19 pneumonia. Our approach eliminates the extra time and resources needed to develop new technology and associated algorithms, thus aiding the front-line healthcare workers in the race against the COVID-19 pandemic. Our results show that DarkNet-19 is the optimal pre-trained neural network for the detection of radiographic features of COVID-19 pneumonia, scoring an overall accuracy of 94.28% over 5,854 X-ray images. We also present a custom visualization of the results that can be used to highlight important visual biomarkers of the disease and disease progression.

Item Metadata

Title
The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias
Creator
Elgendi, Mohamed; Nasir, Muhammad Umer; Tang, Qunfeng; Fletcher, Richard Ribon; Howard, Newton; Menon, Carlo; Ward, Rabab Kreidieh; Nicolaou, Savvas
Contributor
Children's Hospital (Vancouver, B.C.).; BC Women’s Hospital and Health Centre
Publisher
Frontiers in Medicine
Date Issued
2020-08-18
Description
Chest radiography is a critical tool in the early detection, management planning, and follow-up evaluation of COVID-19 pneumonia; however, in smaller clinics around the world, there is a shortage of radiologists to analyze large number of examinations especially performed during a pandemic. Limited availability of high-resolution computed tomography and real-time polymerase chain reaction in developing countries and regions of high patient turnover also emphasizes the importance of chest radiography as both a screening and diagnostic tool. In this paper, we compare the performance of 17 available deep learning algorithms to help identify imaging features of COVID19 pneumonia. We utilize an existing diagnostic technology (chest radiography) and preexisting neural networks (DarkNet-19) to detect imaging features of COVID-19 pneumonia. Our approach eliminates the extra time and resources needed to develop new technology and associated algorithms, thus aiding the front-line healthcare workers in the race against the COVID-19 pandemic. Our results show that DarkNet-19 is the optimal pre-trained neural network for the detection of radiographic features of COVID-19 pneumonia, scoring an overall accuracy of 94.28% over 5,854 X-ray images. We also present a custom visualization of the results that can be used to highlight important visual biomarkers of the disease and disease progression.
Subject
chest X-ray radiography; artificial intelligence; image classification; neural network; convolutional neural networks; corona virus; transfer learning
Genre
Article
Type
Text
Language
eng
Date Available
2021-04-13
Provider
Vancouver : University of British Columbia Library
Rights
Attribution 4.0 International
DOI
10.14288/1.0396675
URI
http://hdl.handle.net/2429/77755
Affiliation
Applied Science, Faculty of; Medicine, Faculty of; Non UBC; Electrical and Computer Engineering, Department of; Obstetrics and Gynaecology, Department of; Radiology, Department of
Citation
Elgendi, M., Nasir, M. U., Tang, Q., Fletcher, R. R., Howard, N., Menon, C., . . . Nicolaou, S. (2020). The performance of deep neural networks in differentiating chest X-rays of COVID-19 patients from other bacterial and viral pneumonias. Frontiers in Medicine, 7 n.18
Publisher DOI
10.3389/fmed.2020.00550
Peer Review Status
Reviewed
Scholarly Level
Faculty; Researcher
Copyright Holder
Authors
Rights URI
http://creativecommons.org/licenses/by/4.0/
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Attribution 4.0 International