Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning

04/20/2020
by   Shervin Minaee, et al.
1

The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the world, having a severe impact on the health and life of many people globally. One of the crucial step in fighting COVID-19 is the ability to detect the infected patients early enough, and put them under special care. Detecting this disease from radiography and radiology images is perhaps one of the fastest way to diagnose the patients. Some of the early studies showed specific abnormalities in the chest radiograms of patients infected with COVID-19. Inspired by earlier works, we study the application of deep learning models to detect COVID-19 patients from their chest radiography images. We first prepare a dataset of 5,000 Chest X-rays from the publicly available datasets. Images exhibiting COVID-19 disease presence were identified by board-certified radiologist. Transfer learning on a subset of 2,000 radiograms was used to train four popular convolutional neural networks, including ResNet18, ResNet50, SqueezeNet, and DenseNet-121, to identify COVID-19 disease in the analyzed chest X-ray images. We evaluated these models on the remaining 3,000 images, and most of these networks achieved a sensitivity rate of 97%(± 5%), while having a specificity rate of around 90%. While the achieved performance is very encouraging, further analysis is required on a larger set of COVID-19 images, to have a more reliable estimation of accuracy rates. Besides sensitivity and specificity rates, we also present the receiver operating characteristic (ROC), area under the curve (AUC), and confusion matrix of each model. The dataset, model implementations (in PyTorch), and evaluations, are all made publicly available for research community, here: https://github.com/shervinmin/DeepCovid.git

READ FULL TEXT

page 1

page 3

research
09/10/2020

COVID CT-Net: Predicting Covid-19 From Chest CT Images Using Attentional Convolutional Network

The novel corona-virus disease (COVID-19) pandemic has caused a major ou...
research
10/03/2020

COVID-19 Classification of X-ray Images Using Deep Neural Networks

In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest ...
research
02/08/2021

Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images

Despite the great efforts to find an effective way for COVID-19 predicti...
research
10/06/2020

COVIDomaly: A Deep Convolutional Autoencoder Approach for Detecting Early Cases of COVID-19

As of September 2020, the COVID-19 pandemic continues to devastate the h...
research
05/21/2021

AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Images

Covid-19 global pandemic continues to devastate health care systems acro...
research
09/05/2023

A Lightweight, Rapid and Efficient Deep Convolutional Network for Chest X-Ray Tuberculosis Detection

Tuberculosis (TB) is still recognized as one of the leading causes of de...
research
03/25/2021

Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19

In the context of the current global pandemic and the limitations of the...

Please sign up or login with your details

Forgot password? Click here to reset