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This paper develops a model that can recognize Parkinson's disease (PD) at its early stage. PD is a common neurodegenerative disorder of the central nervous system that presents with progressive slow movement, tremor, limb rigidity, and gait alterations, including stooped posture, shuffling steps, freezing gait, and falling. There are many methods to detect the presence of pd disease. The proposed predictive analytics framework is a combination of histogram-oriented gradient (HOG) feature descriptor and machine learning technique which is used to gain insights from patients. Our proposed system provides accurate results by integrating spiral and wave drawing inputs of normal and Parkinson's affected patients. From these drawings, HOG feature descriptor extracts the features of input images and machine learning technique is used to classify these images and predict which one is drawn by the person with pd disease with a high accuracy rate.
Keywords:
Convolutional neural networks, Deep Learning.
Cite Article:
"Early detection of Parkinson’s Disease from Spiral and Wave Drawings using Image Processing and Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 4, page no.58 - 63, April-2023, Available :http://www.ijrti.org/papers/IJRTI2304011.pdf
Downloads:
000205282
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator