  Coronavirus disease (COVID-19) is a contagious infection caused by severe
acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and
killed millions of people across the globe. In the absence of specific drugs or
vaccines for the treatment of COVID-19 and the limitation of prevailing
diagnostic techniques, there is a requirement for some alternate automatic
screening systems that can be used by the physicians to quickly identify and
isolate the infected patients. A chest X-ray (CXR) image can be used as an
alternative modality to detect and diagnose the COVID-19. In this study, we
present an automatic COVID-19 diagnostic and severity prediction (COVIDX)
system that uses deep feature maps from CXR images to diagnose COVID-19 and its
severity prediction. The proposed system uses a three-phase classification
approach (healthy vs unhealthy, COVID-19 vs Pneumonia, and COVID-19 severity)
using different shallow supervised classification algorithms. We evaluated
COVIDX not only through 10-fold cross2 validation and by using an external
validation dataset but also in real settings by involving an experienced
radiologist. In all the evaluation settings, COVIDX outperforms all the
existing stateof-the-art methods designed for this purpose. We made COVIDX
easily accessible through a cloud-based webserver and python code available at
https://sites.google.com/view/wajidarshad/software and
https://github.com/wajidarshad/covidx, respectively.
