Open Access

ARTICLE

Prediction Model for Coronavirus Pandemic Using Deep Learning

by Mamoona Humayun^1,*, Ahmed Alsayat²

1 Department of Information Systems, College of Computer and Information Sciences, Jouf University, KSA
2 Department of Computer Science, College of Computer and Information Sciences, Jouf University, KSA

* Corresponding Author: Mamoona Humayun. Email:

Computer Systems Science and Engineering 2022, 40(3), 947-961. https://doi.org/10.32604/csse.2022.019288

Received 08 April 2021; Accepted 14 May 2021; Issue published 24 September 2021

Abstract

The recent global outbreak of COVID-19 damaged the world health systems, human health, economy, and daily life badly. None of the countries was ready to face this emerging health challenge. Health professionals were not able to predict its rise and next move, as well as the future curve and impact on lives in case of a similar pandemic situation happened. This created huge chaos globally, for longer and the world is still struggling to come up with any suitable solution. Here the better use of advanced technologies, such as artificial intelligence and deep learning, may aid healthcare practitioners in making reliable COVID-19 diagnoses. The proposed research would provide a prediction model that would use Artificial Intelligence and Deep Learning to improve the diagnostic process by reducing unreliable diagnostic interpretation of chest CT scans and allowing clinicians to accurately discriminate between patients who are sick with COVID-19 or pneumonia, and also empowering health professionals to distinguish chest CT scans of healthy people. The efforts done by the Saudi government for the management and control of COVID-19 are remarkable, however; there is a need to improve the diagnostics process for better perception. We used a data set from Saudi regions to build a prediction model that can help distinguish between COVID-19 cases and regular cases from CT scans. The proposed methodology was compared to current models and found to be more accurate (93 percent) than the existing methods.

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