LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis-Reference-Cited by-同舟云学术

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

Published:2024-04 Issue: Volume:247 Page:108066
ISSN:0169-2607
Container-title:Computer Methods and Programs in Biomedicine
language:en
Short-container-title:Computer Methods and Programs in Biomedicine

Author:

Wang Xuechao^ORCID,Huang Junqing^ORCID,Chatzakou Marianna^ORCID,Medijainen Kadri^ORCID,Toomela Aaro,Nõmm Sven^ORCID,Ruzhansky Michael^ORCID

Funder

Engineering and Physical Sciences Research Council

Estonian Research Council

FWO

Publisher

Elsevier BV

Reference47 articles.

1. Biochemical aspects of Parkinson's disease;Hornykiewicz;Neurology,1998

2. Early treatment of Parkinson's disease: opportunities for managed care;Murman;Amer. J. Manag. Care,2012

3. Early pharmacologic treatment in Parkinson's disease;Hauser;Amer. J. Manag. Care,2010

4. The clinical symptoms of Parkinson's disease;Sveinbjornsdottir;J. Neurochem.,2016

5. A clinicopathologic study of 100 cases of Parkinson's disease;Hughes;Arch. Neurol.,1993

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1. An innovative approach for parkinson’s disease diagnosis using CNN, NCA, and SVM;Neural Computing and Applications;2024-08-10

2. Forecasting the potential of reclaimed water using signal decomposition and deep learning;Journal of Water Process Engineering;2024-08

3. Utilizing Keras Model for Dynamic Drawing Analysis in Predicting Parkinson’s Disease through Spiral and Wave Patterns;2024 3rd International Conference for Innovation in Technology (INOCON);2024-03-01

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