Covid-19 Detection Based on Lung Lesion Signs in Big Data Processing Environment

The world is going through a global health crisis known as the Covid-19 pandemic. Currently, the outbreak is still evolving in a complicated way with a high spreading speed and new variants appearing constantly. RT-PCR test is preferred to test a patient infected with Covid-19. However, this method depends on many factors such as the time of specimen collection and preservation procedure. The cost to perform the RT-PCR test is quite high and requires a system of specialized machinery for sample analysis. Using deep learning techniques on medial images provides promising results with high accuracy with recent technological advancements. In this study, we propose a deep learning method based on CasCade R-CNN ResNet-101 and CasCade R-CNN EfficientNet in a big data processing environment that accelerates the detection of Covid-19 infections on chest X-rays. Chest X-ray can quickly be performed in most medical facilities and provides important information in detecting suspected Covid-19 cases in an inexpensive way. Experimental results show that the classification of lung lesions infected with Covid-19 has an accuracy of 96% and mAP of 99%. This method effectively supports doctors to have more basis to identify patients infected with Covid-19 for timely treatment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Modeling population dynamics for information dissemination through Facebook

Online social networks such as Facebook and Twitter have become part of our daily lives. Their influence on business, politics, and society is considerable. Sensitive or unreliable information can adversely affect individuals, organizations, and governments. Due to the effects of the Covid-19 epidemic, online news is more plentiful and accessible, which raises concerns about its reliability, quality, and authenticity. This article proposes the use of population dynamics model to study information dissemination on Facebook and a Susceptible-Infected-Recovered (SIR) model to examine information propagation as an outbreak of disease. We investigated 27 datasets with more than 270,000 messages, and the experiments showed that the population dynamics model is suitable for modeling the spread of information. The results revealed that information propagation could occur rapidly;after only 1–2 days. Additionally, we discovered that it is very crucial to find immediate solutions for preventing fake information as soon as it appears. This work enables us to understand the mechanism of information dissemination on social networks. This can help control and prevent the spread of misleading information, avoiding unintended consequences. © 2021 John Wiley & Sons, Ltd.