Paper Info
Title
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.
Year
DOI
Venue
2022
10.15302/J-QB-021-0278
QUANTITATIVE BIOLOGY
Keywords
DocType
Volume
coronavirus, COVID-19, radiology, machine learning, chest X-ray, contagious infection
Journal
10
Issue
ISSN
Citations 
2
2095-4689
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Wajid Arshad Abbasi101.35
Syed Ali Abbas200.34
Saiqa Andleeb302.03