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Parkinson’s disease (PD) is a progressive neuro degenerative condition that impacts millions globally. Early diagnosis is essential for improving treatment outcomes and enabling timely medical interventions. PD is primarily recognized by motoric symptoms, including tremors, muscles stiffness, and slow movements (bradykinesia). These motor impairments are often accompanied by changes in voice frequency and dopamine deficiencies. This project aims to employ a deep learning (DL) method for detection of Parkinson’s disease through both DaTScan imaging and voice analysis. A Convolutional Neural Network (CNN)-based VGG19 model is developed for detecting PD through DaTScan images, while an Artificial Neural Network (ANN) model is trained to analyze voice data to predict PD. This combined approach will serve as a robust model for PD diagnosis, allowing for a comprehensive and reliable detection method.

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Deep Learning for Parkinson’s Detection Through DaTScan Imaging and Voice Data

  • 会议论文

  • 发表日期：

    2025年

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  • Naga Venkata Rama Jayadev Gudupu,
  • G. Kalyani,
  • Guru Sri Sai Charan Edupuganti

摘要

Parkinson’s disease (PD) is a progressive neuro degenerative condition that impacts millions globally. Early diagnosis is essential for improving treatment outcomes and enabling timely medical interventions. PD is primarily recognized by motoric symptoms, including tremors, muscles stiffness, and slow movements (bradykinesia). These motor impairments are often accompanied by changes in voice frequency and dopamine deficiencies. This project aims to employ a deep learning (DL) method for detection of Parkinson’s disease through both DaTScan imaging and voice analysis. A Convolutional Neural Network (CNN)-based VGG19 model is developed for detecting PD through DaTScan images, while an Artificial Neural Network (ANN) model is trained to analyze voice data to predict PD. This combined approach will serve as a robust model for PD diagnosis, allowing for a comprehensive and reliable detection method.