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Related Experiment Video

Updated: Aug 12, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

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.

|January 31, 2023

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‐19 CXR images Internet of Medical Things (IoMT)Xception cybersecurity federated learning privacy transfer 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.