ABSTRACT
Millions of people are infected by the coronavirus disease 2019 (COVID-19) around the world. Within three months of its first report, it rapidly spread worldwide with thousands of deaths. Since that time, not only underdeveloped and developing countries, but also developed countries have suffered from insufficient medical resources and diagnoses. In this circumstance, researchers from medical and engineering fields have tried to develop automatic COVID-19 detection toolkits using machine learning (ML) techniques. The dataset is the fundamental element of any detection tool;therefore, most of the ML-based COVID-19 detection research was conducted used chest x-ray and computed tomography (CT) image datasets. In our study, we collected a series of publicly available unique COVID-19 x-ray and CT image datasets, then assessed and compared their performances using our proposed 22 layer convolutional neural network model along with ResNet-18 and VGG16. We investigated eight individual datasets known as Twitter, SIRM x-ray, COVID-19 Image Repository, EURORAD, BMICV, SIRM CT, COVID-CT, and SARS-CoV-2 CT. Our model obtained classification accuracy of 91%, 81%, 59%, 98%, 58%, 79%, and 97%, respectively. Our proposed model obtained the highest classification accuracy using four datasets (Twitter, COVID-19 Image Repository, COVID-CT, and SARS-CoV-2 CT). Similarly, ResNet-18 only utilized three (EURORAD, BMICV, and SIRM CT), whereas VGG16 only utilized the SIRM x-ray dataset. Results of this investigation indicate a significant comparison chart among the performance of the datasets. Indeed, our study is a large-scale assessment of existing COVID-19 x-ray and CT image datasets. And to the best of our knowledge, this is the first performance comparison study that includes all publicly available COVID-19 datasets.
ABSTRACT
Coronavirus disease 2019 (COVID-19) is the leading pandemic facing the world in 2019/2020;it is caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which necessitates clear understanding of the infectious agent. The virus manifests aggressive behavior with severe clinical presentation and high mortality rate, especially among the elderly and patients living with chronic diseases. In the recent years, the role of gut microbiota, in health and disease, has been progressively studied and highlighted. It is through gut microbiota-organ bidirectional pathways, such as gut-brain axis, gut-liver axis, and gut-lung axis, that the role of gut microbiota in prompting lung disease, among other diseases, has been proposed and accepted. It is also known that respiratory viral infections, such as COVID-19, induce alterations in the gut microbiota, which can influence immunity. Based on the fact that gut microbiota diversity is decreased in old age and in patients with certain chronic diseases, which constitute two of the primary fatality groups in COVID-19 infections, it can be assumed that the gut microbiota may play a role in COVID-19 pathology and fatality rate. Improving gut microbiota diversity through personalized nutrition and supplementation with prebiotics/probiotics will mend the immunity of the body and hence could be one of the prophylactic strategies by which the impact of COVID-19 can be minimized in the elderly and immunocompromised patients. In this chapter, the role of dysbiosis in COVID-19 will be clarified and the possibility of using co-supplementation of personalized prebiotics/probiotics with current therapies will be discussed.