3D computed tomography-based framework for automated grading of COVID-19 infections

Author First name, Last name, Institution

• I. S. Farahat, Luxor University
• A. Sharafeldeen, J.B. Speed School of Engineering
• M. Elsharkawy, J.B. Speed School of Engineering
• A. Soliman, J.B. Speed School of Engineering
• A. Mahmoud, J.B. Speed School of Engineering
• M. Ghazal, Abu Dhabi University
• F. Taher, Zayed University
• W. Aladrousy, Faculty of Computer and Information
• S. Elmougy, Faculty of Computer and Information
• A. S. Tolba, Faculty of Computer and Information
• A. El-Baz, J.B. Speed School of Engineering

Document Type

Article

Source of Publication

Artificial Intelligence Strategies for Analyzing Covid 19 Pneumonia Lung Imaging Volume 2 Engineering and Clinical Approaches

Publication Date

3-1-2026

Abstract

The COVID-19 outbreak caused by SARS-Cov-2, has affected several million people across the world. Artificial intelligence (AI) techniques have aided in the management of the COVID-19 epidemic in a variety of ways. These include diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines. The primary goal of this chapter is to present a 3D computed tomography (CT)-based system for the automated diagnosis of COVID-19 severity levels. First, the lung regions are delineated, followed by the application of a Markov–Gibbs random field (MGRF) model to extract features that differentiate between COVID-19 severity levels. These features are derived by proposing three distinct MGRF models, each of which calculates Gibbs energy by tuning the model using a specific severity category. Subsequently, a neural network model is employed to classify the three Gibbs energy values, thereby identifying COVID-19 severity levels. The system is evaluated on a dataset of 76 COVID-19 patients using a hold-out validation approach. It achieves an overall accuracy of 83.33% and a Cohen’s kappa score of 73.98%, demonstrating the potential of such a system for grading COVID-19 severity.

DOI Link

10.1088/978-0-7503-3799-1ch3

ISBN

[9780750337991, 9780750345408]

Publisher

IOP Publishing

First Page

3

Last Page

3

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

Computer science (0.45), Grading (engineering) (0.45), Medicine (0.44), Artificial intelligence (0.4), Automation (0.36), Computed tomography (0.36), Medical physics (0.34), Medical imaging (0.34), Computer vision (0.31), Radiology (0.3), Automated method (0.26), Medical diagnosis (0.26)

Scopus ID

105036256518

Recommended Citation

Farahat, I. S.; Sharafeldeen, A.; Elsharkawy, M.; Soliman, A.; Mahmoud, A.; Ghazal, M.; Taher, F.; Aladrousy, W.; Elmougy, S.; Tolba, A. S.; and El-Baz, A., "3D computed tomography-based framework for automated grading of COVID-19 infections" (2026). All Works. 7972.
https://zuscholars.zu.ac.ae/works/7972

Indexed in Scopus

yes

Open Access

no