Open Access

ARTICLE

A Deep Learning Framework for COVID-19 Diagnosis from Computed Tomography

Nabila Mansouri^1,2,*, Khalid Sultan³, Aakash Ahmad⁴, Ibrahim Alseadoon⁴, Adal Alkhalil⁴

1 Department of Computer Science, Applied College, University of Ha’il, KSA
2 ReDCAD Laboratory, Sfax University, Sfax, Tunisia
3 Department of Engineering, College of Engineering and Applied Sciences, American University of Kuwait, Kuwait
4 Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, KSA

* Corresponding Author: Nabila Mansouri. Email:

Intelligent Automation & Soft Computing 2022, 34(2), 1247-1264. https://doi.org/10.32604/iasc.2022.025046

Received 09 November 2021; Accepted 29 January 2022; Issue published 03 May 2022

Abstract

The outbreak of novel Coronavirus COVID-19, an infectious disease caused by the SARS-CoV-2 virus, has caused an unprecedented medical, economic, and social emergency that requires data-driven intelligence and decision support systems to counter the subsequent pandemic. Data-driven models and intelligent systems can assist medical researchers and practitioners to identify symptoms of COVID-19 infection. Several solutions based on medical image processing have been proposed for this purpose. However, the most shortcoming of hand craft image processing systems is the lower provided performances. Hence, for the first time, the proposed solution uses a deep learning model that is applied to Computed Tomography (CT) images for the efficient extraction of COVID-19 features. Since there are few patients in the COVID-CT-Dataset, the Convolutional Neural Network (CNN) model cannot undergo further learned to enhance performances. Therefore, the proposed solution works as a pipeline framework involving two steps: (A) baseline classification is provided by a CNN model; (B) baseline results are re-ranked using distances to features vectors of CT image parts. A re-ranking framework is used as additional means of COVID-19 symptom identification. These steps exploit the diversity of different parts of CT images to enhance classification performance. Evaluations of the proposed solution are driven by real world data based on clinical findings in the form of COVID-CT-Dataset images. The results of the evaluation illustrate the streamlined efficiency and accuracy of the proposed solution to the image-based diagnosis of COVID-19 patients. Our findings support smart healthcare solutions–specifically addressing COVID-19 challenges–and provide guidelines to engineer and develop intelligent and autonomous systems.

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