ABSTRACT
Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has been a disaster for humanity, especially in the health sector. Covid-19 is a serious disease, a large number of people lose their lives every day. This disease not only affects one country, but the whole world suffers from this viral disease. In the fight against COVID-19 immediate and accurate screening of infected patients is essential, one of the most widely used screening approaches is chest X-Ray (CXR) which is rated faster and cheaper. This study aims to detect patients suffering from COVID-19 through chest X-Ray using a transfer learning approach, the method used is with several deep residual network architectures such as ResNet50, RexNet100, SSL ResNet50, semi-weakly supervised learning (SWSL) ResNet50, Wide ResNet50, SK ResNet34, ECA ResNet50d, Inception ResNet V2, CSP ResNet50, and ResNest50d. Then the results will be compared with previous studies. The study was conducted ten times using different pre-training and got the best results on the SWSL ResNet50 architecture with an accuracy value of 99.28%, this value increased 6.98% from previous studies, 99.51% F1-Score, 99.41% Precision, 99.61% Sensitivity, and 98.33% Specificity, that means this study obtained better results than previous studies. © 2022, Institute of Advanced Engineering and Science. All rights reserved.
ABSTRACT
Nearly 35 million cases and one million deaths over the nine months of the COVID-19 pandemic have been reported worldwide. Africa and some countries with endemic parasitic infections had a low incidence of COVID-19. By contrast, the United States and several European countries, having a non-endemicity of parasitic infections, recorded a high incidence of COVID-19. Some parasites have an immunomodulatory mechanism that can induce an immune tolerance state in the infected persons by balancing pro-inflammatory and anti-inflammatory responses. Emerging reports also stated that COVID-19 and helminth co-infections may have more hidden outcomes than predictable ones. Hence, the aim of this literature review is to show and identify that an increase in the number of regulatory immune cells due to the immunomodulatory role of a pre-existing parasitic infection could reduce the risk of COVID-19. This study explored the existing literature to determine the role of parasitic infections in modulating the immune response and possibly reduce the risk of COVID-19 infection in endemic countries. The mechanism of immunomodulation by parasites is the increased numbers of Treg cells, M2 macrophages, eosinophils, the Th2 cytokines IL-4 and IL-5, and the pro-inflammatory downregulation of IFN lambda, TNF alpha, and IL-6, which play an essential role in inducing cytokine storms in COVID-19 infection. This condition will probably occur in an individual with parasitic infection in a community with limited facilities and infrastructure to treat parasitic infections, particularly in developing countries. To conclude, in endemic areas, the immunomodulatory effect of parasitic infection to reduce the risk of COVID-19 cases/deaths is a possibility if the host is immunocompetent. Herein, the current knowledge on the immunomodulatory role of COVID-19 and helminth co-infections will be discussed.