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

doi:10.15302/J-QB-021-0278

Quant. Biol.

2022, Vol. 10

Issue (2) : 208-220 https://doi.org/10.15302/J-QB-021-0278

RESEARCH ARTICLE

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

Wajid Arshad Abbasi¹(

), Syed Ali Abbas¹, Saiqa Andleeb², Maryum Bibi¹, Fiaz Majeed³, Abdul Jaleel⁴, Muhammad Naveed Akhtar⁵

¹. Computational Biology and Data Analysis Lab, Department of Computer Sciences & Information Technology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K 13100, Pakistan
². Biotechnology Lab, Department of Zoology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K 13100, Pakistan
³. Department of Software Engineering, University of Gujrat, Gujrat 50700, Pakistan
⁴. Department of Computer Science, (RCET), UET, Lahore 54000, Pakistan
⁵. Computational and Internet Services Division, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 44000, Pakistan

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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.

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

Corresponding Author(s): Wajid Arshad Abbasi

About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Just Accepted Date: 17 December 2021 Online First Date: 17 March 2022 Issue Date: 07 July 2022

Cite this article:

Wajid Arshad Abbasi,Syed Ali Abbas,Saiqa Andleeb, et al. COVIDX: Computer-aided diagnosis of COVID-19 and its severity prediction with raw digital chest X-ray scans[J]. Quant. Biol., 2022, 10(2): 208-220.

URL:

https://academic.hep.com.cn/qb/EN/10.15302/J-QB-021-0278
https://academic.hep.com.cn/qb/EN/Y2022/V10/I2/208

Tab.1 Predictive performance for the classification of healthy vs un-healthy X-ray scans across different machine learning techniques and feature representations using 10-fold CV

Tab.2 Predictive performance for the classification of healthy vs un-healthy X-ray scans across different machine learning techniques and feature representations on an external validation dataset

Fig.1 ROC and PR curves showing predictive performance of COVIDX for the classification of CXR scans (healthy vs unhealthy and COVID-19 vs pneumonia) across different classifiers (SVC, RF, XGB) and DenseNet feature map on an external validation dataset

Tab.3 Predictive performance for the classification of COVID-19 vs pneumonia X-ray scans across different machine learning techniques and feature representations using 10-fold CV

Tab.4 Predictive performance for the classification of COVID-19 vs pneumonia X-ray scans across different machine learning techniques and feature representations on an external validation dataset

Tab.5 Predictive performance for the severity prediction of corono virus across different machine learning techniques and feature representations using 10-fold CV

Fig.2 Confusion matrix depicting COVIDX real performance under the observation of a qualified radiologist

Fig.3 An Adopted framework to develop computer-aided COVID-19 diagnostics and its severity prediction system using machine learning and X-ray scans.

Fig.4 Working flow of COVIDX for the diagnosis of COVID-19 and its severity prediction using chest X-ray scans.

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