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October 1, 2025Scientific ReportsOpen Access

Enhanced EfficientNet-Extended Multimodal Parkinson’s disease classification with Hybrid Particle Swarm and Grey Wolf Optimizer

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Authors

KRK. RaajasreeRJR Jaichandran

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Overview

Novel multimodal classification using deep learning, demonstrating 99.2% accuracy in Parkinson's disease, highlighting the benefits of hybrid optimization.

Key Points

  • The proposed model achieves 99.2% accuracy in classifying Parkinson's disease stages and healthy controls.
  • Utilizing deep learning, the model outperforms existing methods by significant margins, enhancing diagnostic potential.
  • Hybrid Particle Swarm and Grey Wolf Optimizer improve classification efficiency and reduce execution time by one-third.
  • Multimodal input, including MRI and gait scores, ensures a comprehensive approach to disease classification.

Cite This Study

Raajasree et al. (2025) studied this question.

synapsesocial.com/papers/68dd89e6fe798ba2fc4980e2https://doi.org/10.1038/s41598-025-07069-4
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Also Consider

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

  1. 1Hybrid optimization enabled Eff-FDMNet for Parkinson’s disease detection and classification in federated learning2025 · 1 citations
  2. 2Hybrid deep learning novel framework for classification of parkinson’s disease2026
  3. 3Modified graph neural network-oriented optimization model for the classification of PD.2026
  4. 4MultimodalCNN-PD: a Parkinson’s disease diagnostics framework using multimodal convolutional neural network2026
  5. 5Enhanced meta ensemble stacking approach with XGBoost and optuna based detection of Parkinson's disease2026