A HYBRID DEEP LEARNING APPROACH FOR EARLY PARKINSON’S DETECTION FROM HANDWRITING

C Manoj kumar; Gudivada Lokesh; V Sridhar

A HYBRID DEEP LEARNING APPROACH FOR EARLY PARKINSON’S DETECTION FROM HANDWRITING

Authors: C Manoj kumar, Gudivada Lokesh, V Sridhar

Unique Paper ID: 176096
Volume: 11
Issue: 11
PageNo: 4932-4941

Keywords: Disease K-Nearest Neighbour (KNN) Support Vector Machine (SVM) Parkinson’s Disease Deep Learning Convolutional Neural Networks (CNNs).

Abstract:
The Hybrid Deep Learning Approach study presents an enhanced Parkinson’s Disease (PD) detection model using deep transfer learning and genetic algorithm-based feature optimization. Unlike traditional approaches relying on error-prone handcrafted features, the proposed method employs VGG19, InceptionV3, and ResNet50 to extract robust features from NEWHANDPD spiral handwriting images. These features are further refined using a genetic algorithm to improve accuracy. K-Nearest Neighbour (KNN) is then used for classification. While Support Vector Machine (SVM) showed limited accuracy, KNN outperformed significantly. As an extension, hyperparameter tuning was applied to KNN, achieving improved accuracy of 96–98%, compared to the standard KNN’s 92–95%. This hybrid and optimized approach enhances early PD detection, supporting clinical decision-making with higher reliability.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 4932-4941

A HYBRID DEEP LEARNING APPROACH FOR EARLY PARKINSON’S DETECTION FROM HANDWRITING

Available:https://ijirt.org/Article?manuscript=176096

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