ABSTRACT
The number of deaths caused by COVID-19 is still rising. People still cannot predict the end of this pandemic. Finding out people who may be infected and then monitoring their vitals is an emergency matter. Using medical images to make a diagnosis can provide more information than nucleic acid tests can provide, such as what period the patient was in and the lesion site. In this paper, we use wavelet entropy combined with genetic algorithm to make detection of COVID-19 through CT images. In this method, we expect to use wavelet entropy to extract signal features and genetic algorithm to find the optimal solution. The K fold cross-validation method does not need a large amount of data to complete the verification, and small data sets can also achieve the effect. In the last section, we compare our results with other methods, including RBFNN, KELM, BA. The accuracy of this method reached 73.45%, which is higher than other methods for comparison. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The pandemic of coronavirus disease 2019 (COVID-19) is rapidly spreading all over the world. In order to reduce the workload of doctors, chest X-ray (CXR) and chest computed tomography (CT) scans are playing a major role in the detection and following-up of COVID-19 symptoms. Artificial intelligence (AI) technology based on machine learning and deep learning has significantly upgraded recently medical image screening tools, therefore, medical specialists can make clinical decisions more efficiently on COVID-19 infection cases, providing the best protection to patients as soon as possible. This paper tries to cover the latest progress of automated medical imaging diagnosis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. This paper focuses on the combination of X-ray, CT scan with AI, especially the deep-learning technique, all of which can be widely used in the frontline hospitals to fight against COVID-19. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
ABSTRACT
The pandemic of COVID-19 is going on spreading in 2021, which has infected at least 170 million of people around the world. The healthcare systems are overwhelmed due to the virus infection. Luckily, Internet of Things (IoT) is one of the most effective paradigms in the smart world, in which artificial intelligence technology, like cloud computing and big data analysis, is playing a vital role in epidemic prevention and blocking COVID-19 spreading. For example, in terms of remote screening and diagnosis of the COVID-19 patients, AI technology based on machine learning and deep learning has significantly upgraded recently medical equipment and reshapes the workflow with minimal contact to patients, therefore medical specialists can make clinical decisions more efficiently, providing the best protection not only to patients but also specialists themselves. This paper hereby reviews the latest progress of IoT systems combined AI against COVID-19, and it also provide comprehensive detail on how to overcome the epidemic challenges along with directions towards the possible technology trends for future work. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
ABSTRACT
Objective: To analyze the course of disease and epidemiological parameters of COVID-19 and provide evidence for making prevention and control strategies. Methods: To display the distribution of course of disease of the infectors who had close contacts with COVID-19 cases from January 1 to March 15, 2020 in Guangdong Provincial, the models of Lognormal, Weibull and gamma distribution were applied. A descriptive analysis was conducted on the basic characteristics and epidemiological parameters of course of disease. Results: In total, 515 of 11 580 close contacts were infected, with an attack rate about 4.4%, including 449 confirmed cases and 66 asymptomatic cases. Lognormal distribution was fitting best for latent period, incubation period, pre-symptomatic infection period of confirmed cases and infection period of asymptomatic cases; Gamma distribution was fitting best for infectious period and clinical symptom period of confirmed cases; Weibull distribution was fitting best for latent period of asymptomatic cases. The latent period, incubation period, pre-symptomatic infection period, infectious period and clinical symptoms period of confirmed cases were 4.50 (95%CI:3.86-5.13) days, 5.12 (95%CI:4.63-5.62) days, 0.87 (95%CI:0.67-1.07) days, 11.89 (95%CI:9.81-13.98) days and 22.00 (95%CI:21.24-22.77) days, respectively. The latent period and infectious period of asymptomatic cases were 8.88 (95%CI:6.89-10.86) days and 6.18 (95%CI:1.89-10.47) days, respectively. Conclusion: The estimated course of COVID-19 and related epidemiological parameters are similar to the existing data.
Subject(s)
COVID-19 , Contact Tracing , Cohort Studies , Humans , Incidence , Prospective StudiesABSTRACT
Coronavirus disease 2019 brings a huge burden on the medical industry all over the world. In the background of artificial intelligence (AI) and Internet of Things (IoT) technologies, chest computed tomography (CT) and chest X-ray (CXR) scans are becoming more intelligent, and playing an increasingly vital role in the diagnosis and treatment of diseases. This paper will introduce the segmentation of methods and applications. CXR and CT diagnosis of COVID-19 based on deep learning, which can be widely used to fight against COVID-19.
ABSTRACT
Facing the biggest disaster of this century, the coronavirus (COVID-19) pandemic, every school is inevitably seeking any way to keep its students' learning on schedule. Indeed, using ICT to construct an e-learning system emerges as the necessary step. Based on the strategic fit perspective, this article explores the black-box for how a school can apply and integrate its existing resources to implement an effective e-learning working process using ICT and also generate new capabilities to adapt well to the external challenges a disaster brings. Through the collaboration across different departments, applying ICT tools and designing a rotating way of executing them while considering both learning online and in a practical situation, this e-learning implementation construction offers the case study of KW university and its successful adaptation to the coronavirus challenge. This study divided the e-learning implementation process into three stages, and two frameworks, and an analysis model was also constructed consequently. The key findings and implications of this study, as well as suggestions for future research are also offered. © 2020 ACM.