HIERARCHICAL DEEP LEARNING FOR ENHANCED PARKINSON’S DISEASE DETECTION VIA HANDWRITING ANALYSIS

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects motor skills, leading to difficulties in handwriting, drawing, and movement. Early detection of PD is crucial for timely intervention and effective treatment planning. Traditional clinical diagnosis relies on manual observation of drawing patterns, such as spiral and wave tests, which can be subjective and prone to human error. To address this challenge, we propose an automated deep learning framework for Parkinson’s disease detection using convolutional neural networks. In this work, spiral and wave drawings are processed using MobileNetV2, a lightweight yet powerful deep learning architecture optimized for image classification tasks. The model is finetuned on a dataset of healthy and Parkinson’s patients’ drawings, achieving high classification accuracy. Experimental results demonstrate that the spiral classifier (F1) and the wave classifier (F2) both provide reliable predictions, with final test accuracies exceeding 90%. The system further supports real-time predictions through a Flask-based web application, enabling users to upload spiral or wave drawings and receive instant diagnostic feedback with confidence scores. This research highlights the effectiveness of deep learning in medical diagnostics and offers a scalable, efficient, and non-invasive tool for supporting Parkinson’s disease screening.