Predicting mortality risk of Covid-19 patients using chest X-rays. - Olowolayemo, Akeem; Mohammed Raashid Salih, Mohammed Yasin (Kulliyyah of Information and Communication Technology International Islamic University Malaysia, 2023)
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.