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Detection of COVID-19 Disease Using Federated Learning

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2027))

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Abstract

The study investigates the use of Federated Learning (FL) in combination with the AlexNet architecture for the early confirmation of COVID-19 from chest radiography data. A thorough literature review highlights the potential of FL in healthcare and its application in COVID-19 detection, emphasizing privacy preservation and data security. The methodology involves collecting and 80% preprocessing a diverse dataset, partitioning the data among multiple clients, and iteratively training a global model. Experimental results demonstrate the effectiveness of the FL-based AlexNet model, achieving a high accuracy of 92% on a test dataset. Future research should focus on expanding datasets, exploring advanced privacy techniques, optimizing and scaling FL models, developing real-time applications, and integrating FL models with other modalities for improved COVID-19 detection. These advancements can contribute to early detection and diagnosis, supporting healthcare professionals in combating the pandemic.

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Authors and Affiliations

  1. Bansal Institute of Engineering and Technology, Lucknow, India

    Saurabh Dixit & C. L. P. Gupta

Authors
  1. Saurabh Dixit
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  2. C. L. P. Gupta
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Correspondence to Saurabh Dixit .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. University of Derby, Derby, UK

    Aaisha Makkar

  3. University of Derby, Derby, UK

    Myra Conway

  4. Indian Institute of Information Technology, Bhopal, India

    Ashutosh K. Singh

  5. University of Clermont Auvergne, Aubière, France

    Antoine Vacavant

  6. ENSA - Cadi Ayyad University, Marrakesh, Morocco

    Anas Abou el Kalam

  7. Halmstad University, Halmstad, Sweden

    Mohamed-Rafik Bouguelia

  8. Central University of Karnataka, Kalaburagi, India

    Ravindra Hegadi

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Dixit, S., Gupta, C.L.P. (2024). Detection of COVID-19 Disease Using Federated Learning. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2027. Springer, Cham. https://doi.org/10.1007/978-3-031-53085-2_4

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  • DOI: https://doi.org/10.1007/978-3-031-53085-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53084-5

  • Online ISBN: 978-3-031-53085-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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