most citedLSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis

3 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI20233 cited

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…

cs.CV2023

Optimal Image Transport on Sparse Dictionaries

Junqing Huang, Haihui Wang, Andreas Weiermann +1

In this paper, we derive a novel optimal image transport algorithm over sparse dictionaries by taking advantage of Sparse Representation (SR) and Optimal Transport (OT). Concisely,…

math.AP2023

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…

cs.CV2023

Semi-sparsity Priors for Image Structure Analysis and Extraction

Junqing Huang, Haihui Wang, Michael Ruzhansky

Image structure-texture decomposition is a long-standing and fundamental problem in both image processing and computer vision fields. In this paper, we propose a generalized semi-s…

cs.GR2023

Semi-sparsity on Piecewise Constant Function Spaces for Triangular Mesh Denoising

Junqing Huang, Haihui Wang, Michael Ruzhansky

We present a semi-sparsity model for 3D triangular mesh denoising, which is motivated by the success of semi-sparsity regularization in image processing applications. We demonstrat…

stat.ML2023

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…