A new network structure for Parkinson's handwriting image recognition

Abstract

Parkinson's disease (PD) remains a condition without a cure, though its early manifestations can be managed effectively by medical professionals. This underscores the significance of early detection of PD. It has been widely demonstrated that handwriting analysis is a promising avenue for early PD diagnosis. In recent research, there has been a pivot towards leveraging artificial intelligence (AI) technologies for analyzing handwriting images to aid in diagnosing the disease. This study introduces an innovative network architecture specifically designed to capture the nuances of tremor and irregular spacing characteristic of PD patients' handwriting. By incorporating an attention mechanism, this network is capable of prioritizing different areas within the handwriting feature map, according to their diagnostic relevance. This approach significantly enhances the accuracy of detecting PD through handwriting analysis, with our model achieving an impressive mean accuracy rate of 96.5 %. When compared to traditional convolutional neural networks, our attention-based continuous convolutional network model demonstrates a substantial increase in diagnostic precision.