  The novel corona-virus disease (COVID-19) pandemic has caused a major
outbreak in more than 200 countries around the world, leading to a severe
impact on the health and life of many people globally. As of Aug 25th of 2020,
more than 20 million people are infected, and more than 800,000 death are
reported. Computed Tomography (CT) images can be used as a as an alternative to
the time-consuming "reverse transcription polymerase chain reaction (RT-PCR)"
test, to detect COVID-19. In this work we developed a deep learning framework
to predict COVID-19 from CT images. We propose to use an attentional
convolution network, which can focus on the infected areas of chest, enabling
it to perform a more accurate prediction. We trained our model on a dataset of
more than 2000 CT images, and report its performance in terms of various
popular metrics, such as sensitivity, specificity, area under the curve, and
also precision-recall curve, and achieve very promising results. We also
provide a visualization of the attention maps of the model for several test
images, and show that our model is attending to the infected regions as
intended. In addition to developing a machine learning modeling framework, we
also provide the manual annotation of the potentionally infected regions of
chest, with the help of a board-certified radiologist, and make that publicly
available for other researchers.
