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COVIDz: Deep Learning for Coronavirus Disease Detection

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Computational Intelligence Techniques for Combating COVID-19

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

The severe damage caused by COVID-19 has become a reality, and there is no longer a way to save humanity from this epidemic except diagnose and prevention, especially with emergence delay and lack of vaccine recognized by the World Health Organization. Without therapeutic treatment or explicit restorative immunizations for COVID-19, it is fundamental to diagnose the disease at an early stage and quickly seclude patients contaminated with the virus. This study aims at estimating the damage via consistency of chest imaging, which is not always feasible or possible. Here, an application is proposed to solve the problem via a WEB Predictor ‘COVIDz” and a program exploiting deep learning, so as emergency care will be able to systematically bring chest X-ray images and predict the percentage of the absence or presence of COVID-19. The proposed approach (custom VGG model) and our WEB site “COVIDz” objective validation of the suggested solution obtained the best classification efficiency of 99.64%, F-score of 99.2%, precision of 99.28%, MCC of 99.28%, recall of 99.28%, and a specificity value of 100%.

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Acknowledgments

This work is supported by the General Directorate for Scientific Research and Technological Development (DGRSDT), Higher Education Ministry of Algeria, Algiers, Algeria. The authors would also like to thank Professor Abdelhakim Dinar from St. Peter’s Neurology, Albany, New York 12204, and also STIC Laboratory, Faculty of Technology, University of Tlemcen, Algeria.

Conflict of Interest

The authors have no conflict to disclose.

Author information

Authors and Affiliations

  1. Faculty of Sciences and Technology Mustapha Stambouli University, Mascara, Algeria

    Mohammed Anis Oukebdane, Abd Ellah Youcef Taib & Boualem Merabet

  2. Faculty of Sciences and Technology Mustapha Stambouli University, Mascara, Algeria& STIC Lab, University of Tlemcen, Tlemcen, Algeria

    Samir Ghouali

  3. Al Mussaib Technical College, Al Furat Al Awsat Technical University, Al Mussaib, Babil, Iraq

    Emad Kamil Hussein

  4. Faculty of Economics, Business and Management Sciences, MCLDL Laboratory, University of Mascara, Mascara, Algeria

    Mohammed Seghir Guellil

  5. LSTE Laboratory, Faculty of Sciences and Technology Mustapha Stambouli University, Mascara, Algeria

    Amina Elbatoul Dinar

  6. InnoDev (Dev Software), Tlemcen, Algeria

    Walid Cherifi

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  1. Mohammed Anis Oukebdane
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  2. Samir Ghouali
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  3. Emad Kamil Hussein
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Correspondence to Samir Ghouali .

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Editors and Affiliations

  1. LBEF Campus, Kathmandu Nepal; (In Academic Collaboration with Asia Pacific University of Technology & Innovation), Kuala Lumpur, Malaysia

    Sandeep Kautish

  2. Taoyuan Campus, National Taipei University of Business, Taoyuan, Taiwan

    Sheng-Lung Peng

  3. Faculty of Computer Science and Mathematics, Department of Computer Science, University of Kufa, Najaf, Iraq

    Ahmed J. Obaid

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Oukebdane, M.A. et al. (2021). COVIDz: Deep Learning for Coronavirus Disease Detection. In: Kautish, S., Peng, SL., Obaid, A.J. (eds) Computational Intelligence Techniques for Combating COVID-19. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-68936-0_17

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  • DOI: https://doi.org/10.1007/978-3-030-68936-0_17

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  • Online ISBN: 978-3-030-68936-0

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