Leveraging Machine Learning framework and GANs for Parkinson disease detection

Gonsalves, Calista Clifford, Muntean, Cristina Hava, Jilani, Musfira, Cudden, Mark, Pathak, Pramod and Stynes, Paul (2025) Leveraging Machine Learning framework and GANs for Parkinson disease detection. In: Sixth International Conference on Computer Vision and Information Technology (CVIT 2025). Proceedings of SPIE - The International Society for Optical Engineering, 13796 (137960). SPIE, Florence, Italy. ISBN 978-151069472-9

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Abstract

Parkinson's Disease (PD) is a progressive disorder that affects the nervous system, and the parts of the body controlled by nerves. Early-stage detection of PD using spiral and wave images can significantly improve patient outcomes. Current research has identified limitations in the classification of PD such as reduced dataset size. Processing a large and varied dataset and achieving high accuracy in PD detection can be a challenge. This research proposes a machine learning framework to improve the early detection of Parkinson's Disease by improving the accuracy. This research creates a novel dataset called GAN-PD Hybrid Dataset that combines 1632 original handwritten images and 1868 GAN-generated images for both Parkinson's and healthy subjects, used to train hybrid (ResNet50 and InceptionV3 with KNN) and standalone CNN models (ResNet50, InceptionV3). Data pre-processing and transfer learning techniques are applied to two pre-trained CNN models, namely ResNet50 and InceptionV3. Each of these models is evaluated both individually and in combination with KNN classifier. Results of these models are presented in this paper based on accuracy, sensitivity, specificity, F1 score, Cohen’s Kappa and precision. This research shows promise for InceptionV3 in aiding medical practitioners by detecting PD at an early stage.

Item Type:	Book Section
Additional Information:	(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Uncontrolled Keywords:	GAN; InceptionNetV3; Parkinson’s Disease; ResNet50
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RB Pathology Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry > Neurology. Diseases of the Nervous System.
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	18 Dec 2025 16:56
Last Modified:	18 Dec 2025 16:56
URI:	https://norma.ncirl.ie/id/eprint/9059

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