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Related Experiment Video

Updated: Sep 13, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

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

Sangeetha Subramaniam¹, Umarani Balakrishnan²

¹Department of Information Technology, Kongunadu College of Engineering and Technology (Autonomous), Trichy, India.

Network (Bristol, England)

|August 1, 2025

Summary

This study introduces a new AI framework for early Parkinson's Disease (PD) detection and classification. The FedL_WSSO based Eff-FDMNet achieves high accuracy, improving patient diagnosis and outcomes.

Keywords:

Parkinson‘s disease federated learning magnetic resonance imaging water wheel plant optimization

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Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation

Published on: July 16, 2014

Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Parkinson's Disease (PD) is a progressive neurodegenerative disorder.
Early diagnosis is critical for effective symptom management and slowing disease progression.

Purpose of the Study:

To propose a novel framework, FedL_WSSO based Eff-FDMNet, for accurate PD detection and classification.
To leverage federated learning and advanced deep learning models for improved diagnostic reliability.

Main Methods:

Image preprocessing using Gaussian filters and augmentation.
Feature extraction followed by PD detection using ShCNN-Fuzzy-ZFNet.
PD classification via WSSO-trained Eff-FDMNet with CAViaR-based server updates.

Main Results:

Achieved highest accuracy of 0.927.
Obtained mean average precision of 0.905.
Reported lowest false positive rate (FPR) of 0.082, loss of 0.073, MSE of 0.213, and RMSE of 0.461.

Conclusions:

The developed FedL_WSSO based Eff-FDMNet framework demonstrates high accuracy and low error rates for PD detection.
This potent framework has the potential to enhance patient outcomes through reliable and personalized diagnosis.