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Parkinson's disease detection using inceptionV3: A Deep learning approach.

Pallavi M Shanthappa¹, Madhwesh Bayari¹, G B Abhilash¹

¹Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India.

|May 21, 2025

Summary

This study introduces deep learning models to detect Parkinson's disease (PD) using spiral drawings. High accuracy was achieved, offering a non-invasive method for early PD diagnosis.

Keywords:

Convolutional neural network Deep learning InceptionV3 Parkinson’s disease Spiral drawing Analysis

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Area of Science:

Neurology
Computer Science
Biomedical Engineering

Background:

Parkinson's disease (PD) is a progressive neurodegenerative disorder impacting motor function.
Early detection of PD is crucial for effective intervention and improved patient prognosis.
Current diagnostic methods can be invasive or lack accessibility.

Purpose of the Study:

To evaluate the efficacy of deep learning algorithms in classifying spiral images for non-invasive Parkinson's disease detection.
To assess the performance of different Convolutional Neural Network (CNN) architectures in identifying motor impairments characteristic of PD.
To explore the potential of transfer learning in enhancing feature extraction from spiral drawings.

Main Methods:

A dataset of spiral images drawn by individuals with and without PD was curated.
Four CNN architectures (DenseNet121, InceptionV3, VGG16, LeNet) were employed for classification.
Transfer learning was utilized to refine the models' ability to detect subtle motor impairment patterns.

Main Results:

DenseNet121 and InceptionV3 models achieved high classification accuracy (98.44%).
VGG16 demonstrated strong performance in feature extraction capabilities.
Feature scaling and hybrid deep learning models contributed to improved classification accuracy.

Conclusions:

Deep learning offers a consistent, efficient, and automated approach for the early, non-invasive diagnosis of Parkinson's disease.
Spiral image analysis using CNNs shows significant potential as a low-cost diagnostic tool.
Future research could integrate spiral analysis with other biomarkers for comprehensive PD assessment.