The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias

Frontiers in Medicine

article

Mohamed Elgendi¹  Muhammad Umer Nasir⁶  Qunfeng Tang¹  Richard Ribon Fletcher⁷  Newton Howard⁵  Carlo Menon⁴  Rabab Ward¹  William Parker⁸  Savvas Nicolaou⁶ 

[1] School of Electrical and Computer Engineering, University of British Columbia;Department of Obstetrics & Gynaecology, Faculty of Medicine, University of British Columbia;BC Children's & Women's Hospital;School of Mechatronic Systems Engineering, Simon Fraser University;Nuffield Department of Surgical Sciences, University of Oxford, United Kingdom;Department of Emergency and Trauma Radiology, Vancouver General Hospital;D-Lab, Massachusetts Institute of Technology, United States;Department of Radiology, Faculty of Medicine, University of British Columbia

关键词: chest X-ray radiography;    artificial intelligence;    image classification;    neural network;    convolutional neural networks;    corona virus;    transfer learning;   

DOI  :  10.3389/fmed.2020.00550

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY