The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias
Name
fmed-07-00550.pdf
Description
Published version
Size
989.92 KB
Format
Adobe PDF
Checksum (MD5)
1d0520b6e2053b104ce26b44d9314c9e
Author(s)
Elgendi, Mohamed
Nasir, Muhammad Umer
Tang, Qunfeng
Fletcher, Richard R
Howard, Newton
Menon, Carlo
Ward, Rabab
Parker, William
Nicolaou, Savvas
Date Issued
August 2020
Journal
Frontiers in Medicine
Publisher
Frontiers Media SA
Citation
Elgendi, Mohamed et al. "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 (August 2020): 550 © 2020 The Authors
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Device Research Laboratory
MIT Edgerton Center
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/fmed.2020.00550
