Open Access

ARTICLE

A Novel Technique for Early Detection of COVID-19

Mohammad Yamin^1,*, Adnan Ahmed Abi Sen², Zenah Mahmoud AlKubaisy¹, Rahaf Almarzouki¹

1 Department of Management Information Systems, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Faculty of Computer and Information Systems, Islamic University of Madinah, Al Medina, Saudi Arabia

* Corresponding Author: Mohammad Yamin. Email:

Computers, Materials & Continua 2021, 68(2), 2283-2298. https://doi.org/10.32604/cmc.2021.017433

Received 21 January 2021; Accepted 26 February 2021; Issue published 13 April 2021

Abstract

COVID-19 is a global pandemic disease, which results from a dangerous coronavirus attack, and spreads aggressively through close contacts with infected people and artifacts. So far, there is not any prescribed line of treatment for COVID-19 patients. Measures to control the disease are very limited, partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease. Early detection of positive cases is critical in preventing further spread, achieving the herd immunity, and saving lives. Unfortunately, so far we do not have effective toolkits to diagnose very early detection of the disease. Recent research findings have suggested that radiology images, such as X-rays, contain significant information to detect the presence of COVID-19 virus in early stages. However, to detect the presence of the disease in in very early stages from the X-ray images by the naked eye is not possible. Artificial Intelligence (AI) techniques, machine learning in particular, are known to be very helpful in accurately diagnosing many diseases from radiology images. This paper proposes an automatic technique to classify COVID-19 patients from their computerized tomography (CT) scan images. The technique is known as Advanced Inception based Recurrent Residual Convolution Neural Network (AIRRCNN), which uses machine learning techniques for classifying data. We focus on the Advanced Inception based Recurrent Residual Convolution Neural Network, because we do not find it being used in the literature. Also, we conduct principal component analysis, which is used for dimensional deduction. Experimental results of our method have demonstrated an accuracy of about 99%, which is regarded to be very efficient.

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Citations