  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 ways 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 98%
($\pm$ 3%), while having a specificity rate of around 90%. Besides sensitivity
and specificity rates, we also present the receiver operating characteristic
(ROC) curve, precision-recall curve, average prediction, and confusion matrix
of each model. We also used a technique to generate heatmaps of lung regions
potentially infected by COVID-19 and show that the generated heatmaps contain
most of the infected areas annotated by our board certified radiologist. 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. The dataset, model implementations (in PyTorch), and evaluations, are
all made publicly available for research community at
https://github.com/shervinmin/DeepCovid.git

Computer Vision and Pattern Recognition