摘要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.