2021
DOI: 10.32604/cmc.2021.012955
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Abstract: epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and followup. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 viru… Show more

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Cited by 85 publications
(56 citation statements)
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“…Previously, online shopping was only available in a few product categories and to a select group of consumers. The volume of global online commerce has increased significantly, owing to the recent COVID-19 crisis [1,2], which has accelerated the growth of e-commerce. Because of e-commerce growth, the grocery (FMCG) industry is also equipped with advanced technologies such as the Internet of Things (IoT), cloud computing, and blockchain technology.…”
Section: Introductionmentioning
confidence: 99%
“…Previously, online shopping was only available in a few product categories and to a select group of consumers. The volume of global online commerce has increased significantly, owing to the recent COVID-19 crisis [1,2], which has accelerated the growth of e-commerce. Because of e-commerce growth, the grocery (FMCG) industry is also equipped with advanced technologies such as the Internet of Things (IoT), cloud computing, and blockchain technology.…”
Section: Introductionmentioning
confidence: 99%
“…While it has been shown that a mixture of deep architectures can perform improvement on COVID-19 detection 27 , 28 , this section proposes an end-to-end deep architecture model by an efficient combination of CapsNet and DenseNet. Also, we propose to enhance our architecture using a Cost-sensitive loss function and an efficient Regularization.…”
Section: Methodsmentioning
confidence: 99%
“…Al‐Waisy et al ( 2021 ) proposed a parallel architecture (COVID‐DeepNet) based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch with a large‐scale dataset was then integrated. The system accurately diagnosed patients with COVID‐19, with a detection accuracy rate of 99.93%, sensitivity of 99.90%, specificity of 100%, precision of 100%, F1‐score of 99.93%, MSE of 0.021% and RMSE of 0.016%.…”
Section: Machine Learning and Deep Learning For Covid‐19mentioning
confidence: 99%