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.