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<p>Parkinson’s disease (PD) is the second most common neurological ailment and affects a substantial percentage of the elderly population. A deterioration in handwriting is a common indication of Parkinson’s disease. Researchers have developed an algorithm-based tool to assist in distinguishing between Parkinson’s disease patients and healthy persons because the disease can be difficult to identify. Unfortunately, previous studies have not tackled this problem or adopted a preventive approach. Examining handwriting is one way to detect PD. After analyzing multiple publications, we examined the 2020 and 2022 articles from Convolutional Neural Network utilizing deep learning techniques. Spiral and wave photos of those in good health and those with Parkinson’s illness were employed in our collection. This investigation used the Random Forest, VGG-16, and ResNet-50 approaches. We applied ResNet-50 for the drawings and got 97% accuracy for spiral pictures over 10 epochs and 98% accuracy for wave images over eight epochs by using feature extraction and preprocessing techniques. This exceptional performance demonstrates ResNet-50’s potential as a potent tool for medical diagnostics, utilizing its deep learning skills to generate precise and reliable results compared to existing studies in which ResNet-50 has 83% accuracy.</p>

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Deep learning approach for Parkinson’s screening with geometric features from spiral and wave drawings

  • 期刊论文

  • 发表日期：

    2025年5月21日

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  • Meenakshi Malik,
  • Edeh Michael Onyema,
  • Mueen Uddin,
  • Poonam Yadav,
  • Aanchal Sharma,
  • Jazlyn Jose,
  • Achyut Shankar,
  • Fahad Alasim,
  • Mustufa Haider Abidi

摘要

Parkinson’s disease (PD) is the second most common neurological ailment and affects a substantial percentage of the elderly population. A deterioration in handwriting is a common indication of Parkinson’s disease. Researchers have developed an algorithm-based tool to assist in distinguishing between Parkinson’s disease patients and healthy persons because the disease can be difficult to identify. Unfortunately, previous studies have not tackled this problem or adopted a preventive approach. Examining handwriting is one way to detect PD. After analyzing multiple publications, we examined the 2020 and 2022 articles from Convolutional Neural Network utilizing deep learning techniques. Spiral and wave photos of those in good health and those with Parkinson’s illness were employed in our collection. This investigation used the Random Forest, VGG-16, and ResNet-50 approaches. We applied ResNet-50 for the drawings and got 97% accuracy for spiral pictures over 10 epochs and 98% accuracy for wave images over eight epochs by using feature extraction and preprocessing techniques. This exceptional performance demonstrates ResNet-50’s potential as a potent tool for medical diagnostics, utilizing its deep learning skills to generate precise and reliable results compared to existing studies in which ResNet-50 has 83% accuracy.