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  3. Federated Learning Based Covid-19 Detection.
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  3. Federated Learning Based Covid-19 Detection.

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Federated learning based Covid-19 detection.

Deepraj Chowdhury1, Soham Banerjee1, Madhushree Sannigrahi2

  • 1Department of Electronics and Communication International Institute of Information Technology Naya Raipur Naya Raipur Chhattisgarh India.

Expert Systems
|January 31, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a secure deep and federated learning model for rapid COVID-19 detection using chest X-rays. The model achieves 99.59% accuracy, enhancing diagnostic accessibility through the Internet of Medical Things.

Keywords:
COVID‐19CXR imagesInternet of Medical Things (IoMT)Xceptioncybersecurityfederated learningprivacytransfer learning

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Public Health

Background:

  • The global COVID-19 pandemic necessitates accurate and accessible diagnostic tools.
  • Existing diagnostic methods may face challenges in speed and data privacy.
  • The integration of deep learning and federated learning offers potential for secure and efficient medical diagnostics.

Purpose of the Study:

  • To develop a deep and federated learning model for rapid COVID-19 detection from chest X-ray images.
  • To prioritize user data security while maximizing diagnostic accuracy.
  • To leverage the Internet of Medical Things (IoMT) for improved healthcare access.

Main Methods:

  • A sequential Convolutional Neural Network (CNN) model was developed using deep and federated learning principles.
  • The architecture was designed for efficient client and server-side operations, utilizing StreamLit for the front-end and Flower for the back-end.
  • The model was trained over three federated communication rounds.

    • Main Results:

    • The proposed model achieved a global accuracy of 99.59% in detecting COVID-19 from chest X-ray images.
    • The federated learning approach ensured user data security during the training process.
    • The system provides rapid COVID-19 detection within seconds.

    Conclusions:

    • The developed deep and federated learning model offers a highly accurate and secure solution for COVID-19 detection.
    • The integration with IoMT enhances the accessibility of rapid diagnostic services.
    • This approach holds significant potential for improving healthcare delivery during pandemics and beyond.