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Background  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.    Objective  In the absence or inadequate provision of therapeutic treatments of COVID‐19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X‐ray (CXR) image can effortlessly be used as a substitute modality to diagnose the COVID‐19.    Method  In this study, we present an automatic COVID‐19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID‐19 and forecast its severity. The proposed system uses a three‐phase classification approach (healthy vs unhealthy, COVID‐19 vs pneumonia, and COVID‐19 severity) using different conventional supervised classification algorithms.    Results  We evaluated COVIDX through 10‐fold cross‐validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state‐of‐the‐art methods designed for this purpose.    Conclusions  Our proposed method (COVIDX), with vivid performance in COVID‐19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow‐up studies of COVID‐19 infected patients.    Availability  We made COVIDX easily accessible through a cloud‐based webserver and python code available at the site of google and the website of Github.

COVIDX: Computer‐aided diagnosis of COVID‐19 and its severity prediction with raw digital chest X‐ray scans