Abstract
This study investigates the role of deep learning models, particularly MobileNet-v2, in Parkinson's Disease (PD) detection through handwriting spiral analysis. Handwriting difficulties often signal early signs of PD, necessitating early detection tools due to potential impacts on patients' work capacities. The study utilizes a three-fold approach, including data augmentation, algorithm development for simulated PD image datasets, and the creation of a hybrid dataset. MobileNet-v2 is trained on these datasets, revealing higher generalization or prediction accuracy of 84\% with hybrid datasets. Future research will explore the impact of high variability synthetic datasets on prediction accuracies and investigate the MobileNet-v2 architecture's memory footprint for timely inferences with low latency.
Paper Type
Full Paper
DOI
10.62036/ISD.2024.76
Mobilenet-V2 Enhanced Parkinson's Disease Prediction with Hybrid Data Integration
This study investigates the role of deep learning models, particularly MobileNet-v2, in Parkinson's Disease (PD) detection through handwriting spiral analysis. Handwriting difficulties often signal early signs of PD, necessitating early detection tools due to potential impacts on patients' work capacities. The study utilizes a three-fold approach, including data augmentation, algorithm development for simulated PD image datasets, and the creation of a hybrid dataset. MobileNet-v2 is trained on these datasets, revealing higher generalization or prediction accuracy of 84\% with hybrid datasets. Future research will explore the impact of high variability synthetic datasets on prediction accuracies and investigate the MobileNet-v2 architecture's memory footprint for timely inferences with low latency.
Recommended Citation
Bhat, S.A. & Szczuko, P. (2024). Mobilenet-V2 Enhanced Parkinson's Disease Prediction with Hybrid Data Integration. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.76