scholarly journals Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson’s Disease from Speech in Three Different Languages

2019 ◽  
pp. 697-706 ◽  
Author(s):  
Juan Camilo Vásquez-Correa ◽  
Tomas Arias-Vergara ◽  
Cristian D. Rios-Urrego ◽  
Maria Schuster ◽  
Jan Rusz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Learning Strategy

scholarly journals 3D convolutional neural networks for classification of Alzheimer’s and Parkinson’s disease with T1-weighted brain MRI

2021 ◽  
Author(s):  
Nikhil J. Dhinagar ◽  
Sophia I. Thomopoulos ◽  
Conor Owens-Walton ◽  
Dimitris Stripelis ◽  
Jose Luis Ambite ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Brain Mri

scholarly journals Parkinson's Disease Detection Using Isosurfaces-Based Features and Convolutional Neural Networks

2019 ◽  
Vol 13 ◽  
Author(s):  
Andrés Ortiz ◽  
Jorge Munilla ◽  
Manuel Martínez-Ibañez ◽  
Juan M. Górriz ◽  
Javier Ramírez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Disease Detection

Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson’s Disease in 3D Nuclear Imaging Data

2019 ◽  
Vol 29 (09) ◽  
pp. 1950010 ◽  
Author(s):  
Octavio Martinez Manzanera ◽  
Sanne K. Meles ◽  
Klaus L. Leenders ◽  
Remco J. Renken ◽  
Marco Pagani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Positron Emission Tomography ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Fdg Pet ◽  
Characteristic Curve ◽  
Emission Tomography ◽  
Imaging Data ◽  
Positron Emission

Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representation of brain metabolic function. In this study we employed 3D CNNs in FDG-PET brain images with the purpose of discriminating patients diagnosed with Parkinson’s disease (PD) from controls. We employed Scaled Subprofile Modeling using Principal Component Analysis as a preprocessing step to focus on specific brain regions and limit the number of voxels that are used as input for the CNNs, thereby increasing the signal-to-noise ratio in our data. We performed hyperparameter optimization on three CNN architectures to estimate the classification accuracy of the networks on new data. The best performance that we obtained was [Formula: see text] and area under the receiver operating characteristic curve [Formula: see text] on the test set. We believe that, with larger datasets, PD patients could be reliably distinguished from controls by FDG-PET scans alone and that this technique could be applied to more clinically challenging tasks, like the differential diagnosis of neurological disorders with similar symptoms, such as PD, Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA).

scholarly journals Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification

2018 ◽  
Vol 87 ◽  
pp. 67-77 ◽  
Author(s):  
Clayton R. Pereira ◽  
Danilo R. Pereira ◽  
Gustavo H. Rosa ◽  
Victor H.C. Albuquerque ◽  
Silke A.T. Weber ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Disease Identification
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