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:...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 https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/covidx, respectively.展开更多
文摘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 https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/covidx, respectively.
