Parkinson’s Disease (PD) is a progressive neurodegenerative disorder where early detection is essential to slow disease progression and improve patient outcomes. Handwriting analysis, particularly spiral drawings, provides a low-cost, non-invasive biomarker for identifying motor impairments linked to PD. However, existing approaches often depend on multimodal inputs or specialized hardware, limiting scalability and interpretability in clinical use. This study proposes an explainable deep learning framework that detects PD solely from spiral handwriting images.

Using the NewHandPD dataset, a lightweight Convolutional Neural Network (CNN) was trained on grayscale spiral drawings after standard preprocessing steps such as resizing and normalization. The model was evaluated using accuracy, precision, recall, and AUC metrics, and interpretability was ensured through Gradient-Weighted Class Activation Mapping (Grad-CAM), which highlights input regions influencing predictions.

The CNN achieved an overall accuracy of 87%, with a precision of 86.5%, a recall of 87.3%, and an AUC of 0.91 on the test set. Grad-CAM heatmaps confirmed that the network consistently focused on tremor-induced distortions and irregular stroke patterns, aligning with clinically relevant features of PD. This interpretability bridges the gap between deep learning performance and clinical trust, addressing the common “black box” limitation of AI systems in healthcare.

The results demonstrate that handwriting-based biometrics can serve as an effective, explainable, and deployable tool for early PD screening. This work provides a foundation for integrating transparent AI-driven diagnostics into clinical workflows and expanding research toward multimodal approaches in neurodegenerative disease detection.