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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
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Bhat S. A., Szczuko P.: Mobilenet-V2 Enhanced Parkinson's Disease Prediction with Hybrid Data Integration// / : , 2024,
- DOI:
- Digital Object Identifier (open in new tab) 10.62036/isd.2024.76
- Sources of funding:
-
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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