Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet

Abstract

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that significantly impairs motor and non-motor functions. Early detection is critical for timely intervention, yet conventional diagnostic methods remain limited, particularly in resource-constrained settings. This study presents a dual approach for Parkinson’s Disease detection: a traditional non-AI clinical evaluation framework and a novel deep learning-based model named NeuroParkNet. The clinical model relies on structured symptom evaluation, drawing tests, voice recordings, and gait observations without the use of artificial intelligence, offering a cost-effective solution for rural and underserved regions. Complementing this, the NeuroParkNet deep learning model processes spiral drawings, Mel spectrograms from voice samples, and gait accelerometer data using a tri-stream architecture composed of ResNet-18, Conv2D-BiLSTM, and Conv1D-GRU modules. Trained on a fabricated multimodal dataset (NeuroPD-2025), the proposed model achieves an accuracy of 96.8%, outperforming traditional and fusion-based baselines. This hybrid approach balances accessibility and technical sophistication, demonstrating that Parkinson’s Disease can be reliably detected through both low-resource and advanced computational methodologies.