25 citations · 28 across the 5 of their papers we have counts for
5 papers
PointExplainer: Towards Transparent Parkinson's Disease Diagnosis
Xuechao Wang, Sven Nomm, Junqing Huang +3
Deep neural networks have shown potential in analyzing digitized hand-drawn signals for early diagnosis of Parkinson's disease. However, the lack of clear interpretability in exist…
LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis
Xuechao Wang, Junqing Huang, Sven Nomm +4
Background and objectives: Dynamic handwriting analysis, due to its non-invasive and readily accessible nature, has recently emerged as a vital adjunctive method for the early diag…
Comparison of One- Two- and Three- Dimensional CNN models for Drawing-Test-Based Diagnostics of the Parkinson's Disease
Xuechao Wang, Junqing Huang, Marianna Chatzakou +6
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 P…
A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics
Xuechao Wang, Junqing Huang, Marianna Chatzakou +5
In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications. In this paper, we present a light-we…
Semi-Sparsity for Smoothing Filters
Junqing Huang, Haihui Wang, Xuechao Wang +1
In this paper, we propose an interesting semi-sparsity smoothing algorithm based on a novel sparsity-inducing optimization framework. This method is derived from the multiple obser…