3 citations · 4 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…
Towards the Linear Algebra Based Taxonomy of XAI Explanations
Sven Nomm
This paper proposes an alternative approach to the basic taxonomy of explanations produced by explainable artificial intelligence techniques. Methods of Explainable Artificial Inte…