COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology

Abstract

Early diagnosis of Coronavirus disease 2019 (COVID-19) is significantly important, especially in the absence or inadequate provision of a specific vaccine, to stop the surge of this lethal infection by advising quarantine. This diagnosis is challenging as most of the patients having COVID-19 infection stay asymptomatic while others showing symptoms are hard to distinguish from patients having different respiratory infections such as severe flu and Pneumonia. Due to cost and time-consuming wet-lab diagnostic tests for COVID-19, there is an utmost requirement for some alternate, non-invasive, rapid, and discounted automatic screening system. A chest CT scan can effectively be used as an alternative modality to detect and diagnose the COVID-19 infection. In this study, we present an automatic COVID-19 diagnostic and severity prediction system called COVIDC (COVID-19 detection using CT scans) that uses deep feature maps from the chest CT scans for this purpose. Our newly proposed system not only detects COVID-19 but also predicts its severity by using a two-phase classification approach (COVID vs non-COVID, and COVID-19 severity) with deep feature maps and different shallow supervised classification algorithms such as SVMs and random forest to handle data scarcity. We performed a stringent COVIDC performance evaluation not only through 10-fold cross-validation and an external validation dataset but also in a real setting under the supervision of an experienced radiologist. In all the evaluation settings, COVIDC outperformed all the existing state-of-the-art methods designed to detect COVID-19 with an F1 score of 0.94 on the validation dataset and justified its use to diagnose COVID-19 effectively in the real setting by classifying correctly 9 out of 10 COVID-19 CT scans. We made COVIDC openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/covidc.

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

“…3. A similar methodology has already been used in our previous study with CT scans [34]. In the following sections, we will discuss the details of this method.…”

A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

“…In [26], authors designed a system called COVIDC to diagnose COVID-19 based on CNN methodology and using 4882 COVID-19 CT scans as the dataset. The approach involved using a combination of three classifiers: support vector classification (SVC), random forest classification (RFC) and extreme gradient boosting classification (XGBC).…”

McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices

“…In [34] , combination of transfer learning and shallow learning-based approach is used to detect the severity of the COVID-19 infection. The proposed method implemented on cloud-based server detects the binary class of COVID-19 infection severity.…”