Leveraging Machine Learning and GANs for Parkinson disease detection

Gonsalves, Calista Clifford (2024) Leveraging Machine Learning and GANs for Parkinson disease detection. Masters thesis, Dublin, National College of Ireland.

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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. Many previous researchers studied classification of PD using various sources, but they faced a few limitations like reduced dataset size. Developing a machine learning framework that processes a large and varied dataset, maintains computational efficiency, and achieves high accuracy in PD detection can be a challenge. This research proposes a machine learning framework to improve the early detection of PD. The proposed framework uses the combined images generated using Custom GAN (Generative Adversarial Network) and the original dataset with both hybrid (ResNet50 and InceptionV3 with KNN classifier) and standalone pretrained CNN models(ResNet50, InceptionV3). The hybrid model consists of a pretrained CNN model along with a machine learning classifier. This framework makes use of a handwritten Parkinson’s disease dataset (original dataset) that comprises of 1632 images each of Parkinson and healthy, for Custom GAN image generation, pre-trained CNN model training and testing. 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 how this framework can prove useful for patients by detecting PD at an early stage.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Stynes, Paul UNSPECIFIED Jilani, Musfira UNSPECIFIED Cudden, Mark UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > Healthcare Industry 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 > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	18 Aug 2025 15:21
Last Modified:	18 Aug 2025 15:21
URI:	https://norma.ncirl.ie/id/eprint/8571

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