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The degenerative neurological disorder known as Parkinson’s disease (PD) is caused by the loss of neurons in the substantia nigra that produce dopamine. This neuronal decline primarily impacts the dorsal and ventral striatum, impairing both motor and cognitive functions. Affecting 2–3% of individuals aged over 65, PD is the second most prevalent neurodegenerative disorder and is linked to rising mortality rates globally, impacting an estimated 1.4% of the population. The condition manifests through a range of symptoms, including motor dysfunctions like tremors, rigidity, and bradykinesia, as well as non-motor issues such as depression, psychosis, and disrupted sleep. Among the tested deep learning models, VGG16 demonstrated superior performance, achieving a classification accuracy of 96.07% for identifying PD.

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Evaluating the Effectiveness of Deep Learning Models in Parkinson’s Disease Diagnosis Using Magnetic Resonance Imaging

  • 会议论文

  • 发表日期：

    2026年

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  • S. Balamithra,
  • Harisudha Kuresan,
  • S. Murugaveni,
  • V. M. Harshini

摘要

The degenerative neurological disorder known as Parkinson’s disease (PD) is caused by the loss of neurons in the substantia nigra that produce dopamine. This neuronal decline primarily impacts the dorsal and ventral striatum, impairing both motor and cognitive functions. Affecting 2–3% of individuals aged over 65, PD is the second most prevalent neurodegenerative disorder and is linked to rising mortality rates globally, impacting an estimated 1.4% of the population. The condition manifests through a range of symptoms, including motor dysfunctions like tremors, rigidity, and bradykinesia, as well as non-motor issues such as depression, psychosis, and disrupted sleep. Among the tested deep learning models, VGG16 demonstrated superior performance, achieving a classification accuracy of 96.07% for identifying PD.