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Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches pp 43–61Cite as

An Optimized Classification Model for COVID-19 Pandemic Based on Convolutional Neural Networks and Particle Swarm Optimization Algorithm

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Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 322))

Abstract

With the daily rapid growth in the number of newly confirmed and suspected COVID-19 cases, COVID-19 extremely threatens public health, countries’ economic, social life, and international relations around the world. There are different medical methods to detect and diagnose this disease such as viral nucleic acid screening by using specimens of the lower respiratory tract. However, the availability of sufficient laboratory screening in the infested counties represents a critical challenge especially with the fast-spreading of COVID-19. Therefore, alternative diagnostic procedures that depend on Artificial Intelligence (AI) techniques are required in the meantime to fight against this epidemic. This paper focuses on using chest CT for diagnosis of COVID-19, as an alternative or assistive method to the reverse-transcription polymerase chain reaction (RT-PCR) tests. Motivated by this, this paper introduces a new model based on deep learning for detecting patients infected with COVID-19 using chest CT. In this paper, a new proposed model for diagnosis of COVID-19 based on using Convolutional Neural Networks (CNN) and Particle Swarm Optimization (PSO) algorithm to classify the CT chest images of patients into infected or not infected. In this paper, the network hyper-parameters in the CNN are optimized by using the PSO algorithm to eliminate the requirement of manual search and enhance the network performance. The used chest radiography dataset in this paper is described which leveraged to train COVID-Net and includes include more 16,500 chest radiography images across more 13,500 patient cases from two open access data repositories. The experimental results of this work exhibited that the suggested system accuracy ratio of 98.04% is competitive to the other models.

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Author information

Authors and Affiliations

  1. Faculty of Science, Port Said University, Cairo, Egypt

    Walid Hamdy & Ismail Elansary

  2. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  3. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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

    Walid Hamdy, Ismail Elansary, Ashraf Darwish & Aboul Ella Hassanien

Authors
  1. Walid Hamdy
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  2. Ismail Elansary
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  3. Ashraf Darwish
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  4. Aboul Ella Hassanien
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Correspondence to Walid Hamdy .

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Editors and Affiliations

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

    Aboul Ella Hassanien

  2. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

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Hamdy, W., Elansary, I., Darwish, A., Hassanien, A.E. (2021). An Optimized Classification Model for COVID-19 Pandemic Based on Convolutional Neural Networks and Particle Swarm Optimization Algorithm. In: Hassanien, A.E., Darwish, A. (eds) Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches. Studies in Systems, Decision and Control, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-63307-3_3

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