Multiple-Fine-Tuned Convolutional Neural Networks for Parkinson's Disease Diagnosis From Offline Handwriting

Existing decision support system frameworks for diagnosing Parkinson's disease (PD) through handwriting, speech, or gait characteristics share very similar pipelines. Although in some cases, patient data can be captured by commercially available devices, specialized devices or even custom-made prototypes are often required for such tasks. Captured data are used for extracting features that are carefully designed on the basis of domain and problem knowledge. These features are then fed to classifiers that provide a final decision. In this article, we present an approach in which end-to-end processing by a convolutional neural network (CNN) is utilized to diagnose PD from handwriting images, without the use of additional signals. This eliminates any need for specialized devices or feature engineering. To improve the performance of the proposed pretrained CNN, we propose the idea of multiple fine tuning to bridge the gap between semantically different source and target datasets and facilitate more efficient transfer learning. The proposed architecture, which is based on multiple fine tuning and an ensemble of multiple-fine-tuned CNNs, achieves 94.7% accuracy in the classification of PD from offline handwriting.

Automated summary

This article presents an approach using a convolutional neural network to diagnose Parkinson's disease from handwriting images without the need for additional signals or specialized devices. Multiple fine-tuning and ensemble learning are proposed to improve the performance of the pretrained CNN, achieving a high accuracy of 94.7% in classifying PD from offline handwriting.