ABSTRACT

• Parkinson’s Disease (PD) is a progressive neurodegenerative disorder, which is characterized by  Various  symptoms.
• progressive neurodegenerative disorder affects nervous system in the elderly, which is characterized by motor symptoms such as tremor, rigidity, slowness of movement and problems with gait.
• In this work, an attempt has been made to classify the spiral images of healthy control and Parkinson’s disease subjects using deep learning neural network.
• The Vision based Convolutional Neural Network architecture is used to refine the diagnosis of neurodegenerative disorder disease

EXISTING SYSTEM

• The Brain MRI images are trained and tested to give the accuracy Disease measures.
• volumetric analysis is one of the widely used MRI protocols to demonstrate pathological modifications related to PD in the striatal region.

Disadvantages:

• They system does not give the accurate results .
• MRI Imaging involves high cost of production.
• The image resolution is low so the face expression will not be detected.

PROJECT VIDEO

PROPOSED SYSTEM

• Parkinsons disease (PD) is a progressive neurodegenerative disorder of the nervous system in the elderly.
• Vision Based methods have reported promising results in providing better characterization of PD in the early stages and are expected to have better sensitivity than standard clinical measures.
• This project proposes a Vision Based novel deep learning architecture for neuro generative disorder screening.
• In this project, analysis of MRI images for discrimination of healthy control and NDD (Neurodegenerative disorder) subjects is attempted using CNN model.
• The proposed FAST-RCNN exploits Feature Extraction to tackle multi-view data from the Spiral Image data.
• In training, the proposed model employs a data enhancement technology called SCI-KIT’ Image Data Generator API on multi-view data.
• The data features are enriched by this data augmented technology, which can increase the diversity of the experimental samples.

ADVANTAGES

• They recognized faster and more accurately
• The model is trained to learn the low level to high level features and the classification results are validated.
• Thus with a rapid growth in the deep learning architectures, an objective diagnosis of Parkinson’s disease will no longer be a laborious job for the clinicians in the near future.

Software Requirements:

• Front End – Anaconda IDE
• Backend – SQL
• Language – Python 3.8

Hardware Requirements:

• Hard Disk: Greater than 500 GB
• RAM: Greater than 4 GB
• Processor: I3 and Above