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September 10, 2025Network Computation in Neural Systems

Hybrid optimization enabled Eff-FDMNet for Parkinson’s disease detection and classification in federated learning

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Authors

SSSangeetha SubramaniamUBUmarani Balakrishnan

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Overview

This framework improves accuracy in parkinson's disease detection using federated learning and deep learning models.

Key Points

  • The developed method achieved an accuracy of 0.927, indicating effective detection of parkinson's disease.
  • Utilizing hybrid optimization, it reduces the false positive rate to 0.082, proving reliability in diagnosis.
  • Federated learning enables collaborative training without sharing sensitive patient data, ensuring privacy.
  • Image preprocessing techniques like Gaussian filter and augmentation enhance feature extraction for better classification.

Cite This Study

Subramaniam et al. (2025) studied this question.

synapsesocial.com/papers/68c1a90c54b1d3bfb60e24a7https://doi.org/10.1080/0954898x.2025.2514187
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Hybrid optimisation for early Parkinson's disease detection in federated learning2026
  2. 2Enhanced EfficientNet-Extended Multimodal Parkinson’s disease classification with Hybrid Particle Swarm and Grey Wolf Optimizer2025
  3. 3An Ensemble of the Convolutional Neural Network Model with Fuzzy Fusion Rank Algorithm for the Identification of Parkinson’s Disease Using Magnetic Resonance Imaging Images2024
  4. 4Enhanced Parkinson's disease prediction using LDEFS feature selection and Mamdani fuzzy neural network2025
  5. 5Modified graph neural network-oriented optimization model for the classification of PD.2026