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<p>Parkinson’s disease is a progressive neurodegenerative disorder that primarily affects the motor system and is often difficult to diagnose in its early stages due to subtle and age-overlapping symptoms. To address this challenge, we propose a hybrid deep learning-based diagnostic framework using T2-weighted MRI scans from the PPMI dataset consisting of 77 healthy control (HC) and 223 Parkinson’s disease (PD) subjects. The images undergo standardized preprocessing, including intensity normalization and contrast-limited adaptive histogram equalization (CLAHE), to enhance structural visibility in the midbrain region. A hybrid architecture combining EfficientNetB0 for deep feature extraction with an XGBoost classifier is introduced to improve class discrimination across four categories: HCF, HCNF, PDF, and PDNF. The proposed model achieves a testing accuracy of 99.02%, demonstrating strong performance with high precision, recall, and F1-scores across all classes. These results highlight the significance of the hybrid approach in effectively handling limited and imbalanced MRI datasets while improving early PD detection. The framework shows strong potential for deployment as a fast, reliable, and clinically adaptable diagnostic tool.</p>

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Hybrid deep learning novel framework for classification of parkinson’s disease

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月15日

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  • Shivani Desai,
  • Meet Vora,
  • Smit Shah,
  • Kanisha Shah,
  • Tarjni Vyas,
  • Anuja Nair,
  • Hitesh Chhikaniwala,
  • Pranshav Gajjar,
  • Shailesh Gupta,
  • Riki H. Patel,
  • Harshal A. Sanghvi

摘要

Parkinson’s disease is a progressive neurodegenerative disorder that primarily affects the motor system and is often difficult to diagnose in its early stages due to subtle and age-overlapping symptoms. To address this challenge, we propose a hybrid deep learning-based diagnostic framework using T2-weighted MRI scans from the PPMI dataset consisting of 77 healthy control (HC) and 223 Parkinson’s disease (PD) subjects. The images undergo standardized preprocessing, including intensity normalization and contrast-limited adaptive histogram equalization (CLAHE), to enhance structural visibility in the midbrain region. A hybrid architecture combining EfficientNetB0 for deep feature extraction with an XGBoost classifier is introduced to improve class discrimination across four categories: HCF, HCNF, PDF, and PDNF. The proposed model achieves a testing accuracy of 99.02%, demonstrating strong performance with high precision, recall, and F1-scores across all classes. These results highlight the significance of the hybrid approach in effectively handling limited and imbalanced MRI datasets while improving early PD detection. The framework shows strong potential for deployment as a fast, reliable, and clinically adaptable diagnostic tool.