  Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory
syndrome coronavirus 2 virus (SARS-CoV-2). The virus transmits rapidly; it has
a basic reproductive number R of 2.2-2.7. In March 2020, the World Health
Organization declared the COVID-19 outbreak a pandemic. COVID-19 is currently
affecting more than 200 countries with 6M active cases. An effective testing
strategy for COVID-19 is crucial to controlling the outbreak but the demand for
testing surpasses the availability of test kits that use Reverse Transcription
Polymerase Chain Reaction (RT-PCR). In this paper, we present a technique to
screen for COVID-19 using artificial intelligence. Our technique takes only
seconds to screen for the presence of the virus in a patient. We collected a
dataset of chest X-ray images and trained several popular deep convolution
neural network-based models (VGG, MobileNet, Xception, DenseNet,
InceptionResNet) to classify the chest X-rays. Unsatisfied with these models,
we then designed and built a Residual Attention Network that was able to screen
COVID-19 with a testing accuracy of 98% and a validation accuracy of 100%. A
feature maps visual of our model show areas in a chest X-ray which are
important for classification. Our work can help to increase the adaptation of
AI-assisted applications in clinical practice. The code and dataset used in
this project are available at
https://github.com/vishalshar/covid-19-screening-using-RAN-on-X-ray-images.
