Unveiling Parkinson's: Handwriting Symptoms with Explainable and Interpretable CNN Model.

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder globally, afflicting approximately 10 million individuals, necessitates early detection for optimal management. In this paper, we propose deep learning models to discern Parkinson's disease through the nuanced analysis of handwriting with the overall objective of achieving transparency and trustworthiness through the integration of Explainable and Interpretable AI. Leveraging transfer learning from well-established VGG16 and VGG19 architectures and introducing two bespoke CNN models (PD-Detect1 and PD-Detect2), we meticulously scrutinize diverse datasets (HandPD, NewhandPD, Parkinson Drawing) to ascertain the efficacy of our approach. LIME and SHAP Explainable AI techniques are employed to pinpoint specific regions of the spiral drawings that significantly influence the predictions made by the VGG16 and PD-Detect2 models. Additionally, Convolutional Filter Visualization and Grad-CAM are utilized to illustrate how the convolutional layers of the PD-Detect2 model function. Finally, LIME is applied to the PD-Detect2 model to identify visual markers of handwriting symptoms in the spiral drawing. Remarkable results underscore our reliance on VGG16 and VGG19 for precise identification, achieving an outstanding 100% accuracy in the waves drawing dataset. PD-Detect1 exhibits commendable performance with an accuracy of 94.44% in the meander of NewhandPD dataset, while VGG16 achieves an accuracy of 95%. VGG16 records 95% accuracy while PD-Detect2 achieves 85% accuracy in the spiral drawing dataset. Both results further bolstered to 100% with the application of classic data augmentation techniques. The Positive/Negative superpixels from LIME and SHAP highlight the key regions used by VGG16 and PD-Detect2 for predictions, while PD-Detect2 places more emphasis on disease-related features. Visualizing convolutional filters provided insight into the functionality of each layer within the PD-Detect2 model, while the Class Activation Maps produced by Grad-CAM highlighted the image regions most influential to the model's decision. Ultimately, LIME's superpixels identified visual markers of handwriting symptoms associated with Parkinson's disease. Explainable AI and Interpretable AI enhance the quality of CNN models and support the decision-making process, enabling healthcare professionals to more accurately assess disease probability and monitor treatment responses. This leads to a more effective system for the early diagnosis of Parkinson's disease through prediction and visual monitoring of handwriting symptoms. [ABSTRACT FROM AUTHOR]