Diagnosis of Parkinson's Disease Based on Hybrid Fusion Approach of Offline Handwriting Images

Handwriting images are commonly used to diagnose Parkinson's disease due to their intuitive nature and easy accessibility. However, existing methods have not explored the potential of the fusion of different handwriting image sources for diagnosis. To address this issue, this study proposes a hybrid fusion approach that makes use of the visual information derived from different handwriting images and handwriting templates, significantly enhancing the performance in diagnosing Parkinson's disease. The proposed method involves several key steps. Initially, different preprocessed handwriting images undergo pixel-level fusion using Laplacian transformation. Subsequently, the fused and original images are fed into a pre-trained CNN separately to extract visual features. Finally, feature-level fusion is performed by concatenating the feature vectors extracted from the flatten layer, and the fused feature vectors are input into SVM to obtain classification results. Our experimental results validate that the proposed method achieves excellent performance by only utilizing visual features from images, with 95.45% accuracy on the NewHandPD. Furthermore, the results obtained on our dataset verify the strong generalizability of the proposed approach.