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An H2O’s Deep Learning-Inspired Model Based on Big Data Analytics for Coronavirus Disease (COVID-19) Diagnosis

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Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach

Part of the book series: Studies in Big Data ((SBD,volume 78))

Abstract

The outbreak of coronavirus diseases (COVID-19) has rabidly spread all over the world. The World Health Organization (WHO) has announced that coronavirus COVID-19 is an international pandemic. Big Data analytics tools must handle and analyse the massive amount of big medical data, generated daily, quickly due to the fact that time is very significant issue in healthcare applications. In addition, several deep learning algorithms are used along with big data analysis processes to help in detecting COVID-19 outbreaks and predicting their worldwide spread. Many researchers developed their models to diagnosis COVID-19 using Computed Tomography (CT) or X-ray imaging. This chapter presents a detailed discussion of Deep Learning and Big Data Analytics effects in containment of the disease. In addition, an H2O’s Deep-Learning-inspired model based on Big Data analytics (DLBD-COV) is proposed for early diagnosis of COVID-19 cases using CT or X-ray images. The proposed diagnosis model is build based on the machine learning framework (H2O) for scalable processing. The Generative Adversarial Networks (GAN) and the Convolutional Neural Networks (CNNs) are used and their classification results are compared. The experimental results emphasize the superiority of DLBD-COV when using H2O framework for scalable COVID19 classification. The results obtained, using a dataset with thousands of real data and images, show encouraging performance using the automated feature extraction of deep learning techniques used in DLBD-COV.

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Author information

Authors and Affiliations

  1. Computer Engineering Department, MISR Higher Institute for Engineering and Technology, Mansoura, Egypt

    Sally Elghamrawy

  2. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Sally Elghamrawy

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  1. Sally Elghamrawy
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Correspondence to Sally Elghamrawy .

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Editors and Affiliations

  1. Department of Information Technology, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul-Ella Hassanien

  2. Department of Computer Science and Engineering, JIS University, Kolkata, India

    Nilanjan Dey

  3. Department of Computer Engineering, MISR Higher Institute for Engineering and Technology, Mansoura, Egypt

    Sally Elghamrawy

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Elghamrawy, S. (2020). An H2O’s Deep Learning-Inspired Model Based on Big Data Analytics for Coronavirus Disease (COVID-19) Diagnosis. In: Hassanien, AE., Dey, N., Elghamrawy, S. (eds) Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach. Studies in Big Data, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-55258-9_16

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  • DOI: https://doi.org/10.1007/978-3-030-55258-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-55257-2

  • Online ISBN: 978-3-030-55258-9

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