IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

A Novel Approach to Parkinson's Disease Progression Evaluation Using Convolutional Neural Networks

A Novel Approach to Parkinson's Disease Progression Evaluation Using Convolutional Neural Networks
View Sample PDF
Author(s): Mhamed Zineddine (INSA Euromed, Euromed Research Center, Euromed University of Fes, Morocco)
Copyright: 2023
Volume: 11
Issue: 1
Pages: 26
Source title: International Journal of Software Innovation (IJSI)
Editor(s)-in-Chief: Roger Y. Lee (Central Michigan University, USA)and Lawrence Chung (The University of Texas at Dallas, USA)
DOI: 10.4018/IJSI.315655

Purchase

View A Novel Approach to Parkinson's Disease Progression Evaluation Using Convolutional Neural Networks on the publisher's website for pricing and purchasing information.

Abstract

Parkinson's disease (PD) is a devastating disorder with serious impacts on the health and quality of life for a wide group of patients. While the early diagnosis of PD is a critical step in managing its symptoms, measuring its progression would be the cornerstone for the development of treatment protocols suitable for each patient. This paper proposes a novel approach to digital PPMI measures and its combination with spirals drawings to increase the accuracy rate of a neural network to the maximum possible. The results show a well performing CNN model with an accuracy of 1(100%). Thus, the end-users of the proposed approach could be more confident when evaluating the progression of PD. The trained, validated, and tested model was able to classify the PD's progression as High, Medium, or Low, with high sureness.

Related Content

. © 2024.
Yogesh M. Kamble, Raj B. Kulkarni. © 2024. 10 pages.
Zachary Estreito, Vinh Le, Frederick C. Harris Jr., Sergiu M. Dascalu. © 2024. 15 pages.
Chase D. Carthen, Araam Zaremehrjardi, Vinh Le, Carlos Cardillo, Scotty Strachan, Alireza Tavakkoli, Frederick C. Harris Jr., Sergiu M. Dascalu. © 2024. 14 pages.
Partha Ghosh, Takaaki Goto, Leena Jana Ghosh, Giridhar Maji, Soumya Sen. © 2024. 15 pages.
Megha Bhushan, Utkarsh Verma, Chetna Garg, Arun Negi. © 2024. 14 pages.
Deepkiran Munjal, Laxman Singh, Mrinal Pandey, Sachin Lakra. © 2023. 25 pages.
Body Bottom