International Journal of Research and Development in Engineering Sciences (IJRDES ) | A Hybrid Deep Learning Approach for Early Parkinson’s Detection from Handwriting

A Hybrid Deep Learning Approach for Early Parkinson’s Detection from Handwriting

Neurological disorder primarily caused by the depletion of dopamine a neurotransmitter essential for regulating movement and coordination. As dopamine-producing cells in the brain’s basal ganglia deteriorate, individuals begin to experience symptoms such as tremors, muscle stiffness, speech difficulties, and postural instability. Early signs like finger tremors often affect handwriting, leading to micrographic a condition where writing becomes small and cramped. This subtle change can serve as a key indicator for early detection. However, diagnosing PD in its initial stages remains challenging due to the lack of definitive clinical tests. With the evolution of artificial intelligence, particularly deep learning, the potential for early and accurate diagnosis has significantly improved. Convolutional Neural Networks (CNNs) and transfer learning methods now enable the automated analysis of medical data, including handwriting patterns. These approaches have shown exceptional accuracy and promise in identifying PD, offering hope for better symptom management and timely intervention.

Research Type: Classification Research
Paper Type: Interpretative Paper
Vol.7 , Issue 4 , Pages: 1 - 4, Jul 2025
Published on: 02 Jul, 2025
Issue Type: Regular

Cite Score

:

100
No. of authors

:

75
No. of Downloads

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43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

About Authors:

Manoj Kumar

India

Viswam Engineering College

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*Corresponding Author: Manoj Kumar, manuc689@gmail.com

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Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.