Comparison of One- Two- and Three- Dimensional CNN models for Drawing-Test-Based Diagnostics of the Parkinson's Disease

TL;DR

This study compares 1D, 2D, and 3D CNN models for diagnosing Parkinson's disease from drawing tests, finding 3D CNNs achieve the highest accuracy across datasets.

Contribution

It introduces the first application of 3D CNNs to drawing-test-based Parkinson's diagnosis and compares their performance with 1D and 2D models.

Findings

3D CNNs outperform 1D and 2D models in accuracy.

The highest accuracy achieved was 86.67% on the PaHaW dataset.

The study demonstrates the potential of 3D models for improved diagnosis.

Abstract

Subject: In this article, convolutional networks of one, two, and three dimensions are compared with respect to their ability to distinguish between the drawing tests produced by Parkinson's disease patients and healthy control subjects. Motivation: The application of deep learning techniques for the analysis of drawing tests to support the diagnosis of Parkinson's disease has become a growing trend in the area of Artificial Intelligence. Method: The dynamic features of the handwriting signal are embedded in the static test data to generate one-dimensional time series, two-dimensional RGB images and three-dimensional voxelized point clouds, and then one-, two-, and three-dimensional CNN can be used to automatically extract features for effective diagnosis. Novelty: While there are many results that describe the application of two-dimensional convolutional models to the problem, to the best knowledge of the authors, there are no results based on the application of three-dimensional models and very few using one-dimensional models. Main result: The accuracy of the one-, two- and three-dimensional CNN models was 62.50%, 77.78% and 83.34% in the DraWritePD dataset (acquired by the authors) and 73.33%, 80.00% and 86.67% in the PaHaW dataset (well known from the literature), respectively. For these two data sets, the proposed three-dimensional convolutional classification method exhibits the best diagnostic performance.