• Banoth Prashanth
• B. Laxmi Priya
• Aredla Anjali
• Dikonda Swathi
• K. Keerthana

Abstract: Over 6 A neurological disorder called Parkinson's disease affects millions of individuals globally. Early-stage Parkinson's disease patients have higher death rates because there is currently no approved diagnosis for the condition. For this reason, an early diagnosis approach for Parkinson's disease is essential. Research has shown a direct relationship between the degree of handwriting impairment experienced by individuals with Parkinson's disease (PD) and how severe it is. The reason for this is that people who have Parkinson's disease (PD) write far more slowly and with less effort. Therefore, early detection of the condition will result in a more precise clinical diagnosis of Parkinson's disease. Consequently, a system design is proposed to investigate spiral and wave drawing patterns in healthy subjects as well as patients with Parkinson's disease. This technique evaluates the Using two different convolutional neural networks, the spiral and wave drawings' drawing patterns are created. We leverage the TensorFlow deep learning library for Python to create our neural network for severity classification. Keywords: TensorFlow, handwriting disability, and Parkinson’s illness.

TensorFlow, handwriting disability, and Parkinson’s illness.

"Deep Learning-Based Assessment of Parkinson's Disease Severity", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.h231-h238, April-2024, Available :http://www.jetir.org/papers/JETIR2404729.pdf

"Deep Learning-Based Assessment of Parkinson's Disease Severity", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. pph231-h238, April-2024, Available at : http://www.jetir.org/papers/JETIR2404729.pdf