COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

Al-Waisy, Alaa S.; Mohammed, Mazin Abed; Al-Fahdawi, Shumoos; Maashi, Mashael; Garcia‐Zapirain, Begonya; Abdulkareem, Karrar Hameed; Mostafa, Salama A.; Kumar, Nallapaneni Manoj; Le, Dac‐Nhuong

doi:10.32604/cmc.2021.012955

Cited by 92 publications

(61 citation statements)

References 30 publications

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“…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%

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

et al. 2021

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COVID‐19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID‐19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID‐19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID‐19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID‐19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID‐19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID‐19 and inspire their works for future development.

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Section: Machine Learning and Deep Learning For Covid‐19mentioning

confidence: 99%

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

et al. 2021

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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%

Impact of Perceived Risk on Consumers Technology Acceptance in Online Grocery Adoption amid COVID-19 Pandemic

Habib

Hamadneh

2021

Sustainability

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E-commerce industry has witnessed a phenomenal growth globally due to the sudden spread of the COVID-19 pandemic and the advancement of mobile Internet technology, with fast adaption of online shopping technologies by the customers. Previously, online shopping was only available in a few product categories and to a select group of consumers. The COVID-19 guidelines related to safety, physical distancing, closure, lockdown, and other restrictions have insisted that consumers shop online. 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 block chain technology. This paper analyzes the UTAUT2 model and its influence on perceived risk and consumer trust in online purchase intention of grocery categories of products among Indian customers. We tried to analyze the growth potential of new technologies in grocery retail and formulated the hypotheses. The results showed that the spread of COVID-19 pandemic had a significant influence on the online shopping behavior of Indian customers. The outcome of the study partly assists businesses in understanding the impact of the factors of consumer adaption of technology, perceived risk associated with online transaction, consumer trust in online technologies and consumer online purchase intention of grocery products. To promote e-commerce in India, the current study suggests that marketers should try to develop consumer trust and lowering the perceived risk associated with online shopping. Some management implications and future area of study based on empirical findings are also highlighted in the present research work.

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“…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%

COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet

Javidi

Abbaasi

Atashi

et al. 2021

Sci Rep

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With the presence of novel coronavirus disease at the end of 2019, several approaches were proposed to help physicians detect the disease, such as using deep learning to recognize lung involvement based on the pattern of pneumonia. These approaches rely on analyzing the CT images and exploring the COVID-19 pathologies in the lung. Most of the successful methods are based on the deep learning technique, which is state-of-the-art. Nevertheless, the big drawback of the deep approaches is their need for many samples, which is not always possible. This work proposes a combined deep architecture that benefits both employed architectures of DenseNet and CapsNet. To more generalize the deep model, we propose a regularization term with much fewer parameters. The network convergence significantly improved, especially when the number of training data is small. We also propose a novel Cost-sensitive loss function for imbalanced data that makes our model feasible for the condition with a limited number of positive data. Our novelties make our approach more intelligent and potent in real-world situations with imbalanced data, popular in hospitals. We analyzed our approach on two publicly available datasets, HUST and COVID-CT, with different protocols. In the first protocol of HUST, we followed the original paper setup and outperformed it. With the second protocol of HUST, we show our approach superiority concerning imbalanced data. Finally, with three different validations of the COVID-CT, we provide evaluations in the presence of a low number of data along with a comparison with state-of-the-art.

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COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

Cited by 92 publications

References 30 publications

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

Impact of Perceived Risk on Consumers Technology Acceptance in Online Grocery Adoption amid COVID-19 Pandemic

COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet

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