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

Abstract

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.

Author summary

Coronavirus disease (COVID-19) is a contagious infection that has killed masses across the world. Due to the lack of specific therapeutics for the treatment of COVID-19, timely diagnosis of the infection is critical to circumvent its further surge by recommending isolation or quarantine. The normal clinical screening test to diagnose COVID-19 is complex, manual, costly, and laborious. To combat this, we have proposed a machine learning based computer-aided diagnosis (CAD) system called COVIDX (COVID-19 Detection using X-ray images) to identify coronavirus and predict its severity using digital X-ray scans. The performance of the proposed system has been verified rigorously through different measures. The well verified solution is publically available through a cloud based webserver for free use.

Graphical abstract

Keywords

coronavirus / COVID-19 / radiology / machine learning / chest X-ray / contagious infection

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ACKNOWLEDGEMENTS

The authors are highly thankful to all those who have given free access to their compiled and annotated X-ray image datasets.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Wajid Arshad Abbasi, Syed Ali Abbas, Saiqa Andleeb, Maryum Bibi, Fiaz Majeed, Abdul Jaleel and Muhammad Naveed Akhtar declare that they have no conflict of interest or financial conflicts to disclose.

All procedures performed in studies involving animals were in accordance with the ethical standards of the institution or practice at which the studies were conducted, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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