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Related Experiment Video

Updated: May 13, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Federated learning with swarm intelligence for efficient and secure medical image analysis.

M A SayedElahl1, R M Farouk2, Abd Elmounem Ali2

  • 1Department of Computer Science, Faculty of Computers and Information, Damanhour University, Damanhour, Egypt. mohamed.abdelatif@cis.dmu.edu.eg.

Scientific Reports
|May 11, 2026
PubMed
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This study introduces a federated learning framework using swarm intelligence for medical image analysis. It achieves high accuracy in diagnosing COVID-19, monkeypox, and breast cancer while enhancing privacy and reducing communication costs.

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Federated Learning

Background:

  • Healthcare collaborative learning is hindered by strict regulations and fragmented data.
  • Analyzing medical images requires robust, privacy-preserving methods.
  • Existing frameworks struggle to balance privacy, communication efficiency, and diagnostic accuracy.

Purpose of the Study:

  • To develop a federated learning framework augmented with swarm intelligence for enhanced medical image analysis.
  • To optimize hyperparameters, feature selection, and client aggregation weights simultaneously.
  • To ensure a balance between patient privacy, communication costs, and classification accuracy.

Main Methods:

  • Combined Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) with deep Convolutional Neural Networks (CNNs).
Keywords:
Deep learningFederated learningMedical image analysisPrivacy-preserving AISwarm intelligence

Related Experiment Videos

Last Updated: May 13, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

  • Tested the framework on COVID-19, monkeypox, and breast cancer datasets across simulated healthcare institutions.
  • Implemented robust privacy measures and statistical validation.

    • Main Results:

    • Achieved high diagnostic accuracy: 96.71% for COVID-19, 96.06% for monkeypox, and 97.0% for breast cancer.
    • Reduced communication rounds by 25-30% and demonstrated resilience against noise and attacks.
    • Privacy-utility analysis showed acceptable trade-offs with accuracy above 94%.

    Conclusions:

    • The proposed federated learning framework effectively enhances medical image analysis while preserving patient privacy.
    • This approach is suitable for smaller healthcare settings, enabling AI adoption without compromising data security.
    • The study demonstrates a viable solution for secure and efficient collaborative medical AI.