Predicting mortality risk of Covid-19 patients using chest X-rays.

Olowolayemo, Akeem and Mohammed Raashid Salih, Mohammed Yasin (2023) Predicting mortality risk of Covid-19 patients using chest X-rays. International Journal on Perceptive and Cognitive Computing, 9 (1). pp. 33-43. E-ISSN 2462-229X

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Abstract

The outbreak of COVID-19 in late 2019 presents a challenging dimension exhibited by its fast and high rate of infection, even though its severity on infected patients is somewhat feeble, especially in people with strong immunity. Due to this rapid infection rate and limited capacity of healthcare infrastructures, an optimal allocation of health facilities and resources becomes imperative. Consequently, forecasting an individual’s infection severity is crucial to efficiently determine whether the patient requires hospitalization or may be treated as an outpatient to free resources for those desperately deserving. Without such systems, health resources would be inefficiently utilized, resulting in needlessly lost lives. This study attempts to provide a model to determine the mortality of an infected patient on arrival to health facilities to determine whether A Convolutional Neural Networks (CNNs) model based on the ResNet-18 architecture was trained on chest X-rays of COVID-19 patients to estimate their mortality risk, with the best model achieving a training accuracy of 99.6 percent while the validation accuracy achieved is 86.7% along with high sensitivity.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Deep Learning, Convolutional Neural Networks (CNNs), Image Classification, X-Rays, COVID-19 Mortality.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr Akeem Olowolayemo
Date Deposited:	30 Jan 2023 12:44
Last Modified:	20 Nov 2023 09:23
URI:	http://irep.iium.edu.my/id/eprint/101737

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