Deep Learning Approach to Parkinson’s Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification

2019 ◽  
Author(s):  
Marek Wodzinski ◽  
Andrzej Skalski ◽  
Daria Hemmerling ◽  
Juan Rafael Orozco-Arroyave ◽  
Elmar Noth
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Disease Detection ◽  
Learning Approach

Convolutional neural network ensemble for Parkinson's disease detection from voice recordings

2021 ◽  
pp. 105021
Author(s):  
Máté Hireš ◽  
Matej Gazda ◽  
Peter Drotár ◽  
Nemuel Daniel Pah ◽  
Mohammod Abdul Motin ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Network ◽  
Disease Detection ◽  
Neural Network Ensemble

scholarly journals Convolutional Neural Network Optimize the Application of Diffusion Kurtosis Imaging in Parkinson’s Disease

2021 ◽  
Author(s):  
Junyan Sun ◽  
Ruike Chen ◽  
Qiqi Tong ◽  
Jinghong Ma ◽  
Linlin Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Model Fitting ◽  
Diffusion Kurtosis Imaging ◽  
Clinical Assessments ◽  
Fitting Method ◽  
Diffusion Kurtosis

Abstract Objectives: This work attempted to assess the feasibility of deep-learning based method in detecting the alterations of diffusion kurtosis measurements associated with Parkinson's disease (PD). Methods: A group of 68 PD patients and 77 healthy controls (HCs) were scanned on the scanner-A (3T Skyra) (DATASET-1). Meanwhile, an additional 5 healthy travelling volunteers were scanned with both the scanner-A and an additional scanner-B (3T Prisma) (DATASET-2). Diffusion kurtosis imaging (DKI) of DATASET-2 has an extra b shell than that of DATASET-1. In addition, a 3D convolutional neural network (CNN) was trained from Dataset 2 to harmonize the quality of scalar measures of scanner-A to a similar level of scanner-B. Whole-brain unpaired t-test and Tract-Based Spatial Statistics (TBSS) was performed to validate the differences between PD and control groups with model fitting method and CNN method respectively. We further clarified the correlation between clinical assessments and DKI results.Results: In the left substantia nigra (SN), an increase of mean diffusivity (MD) was found in PD group. In the right SN, fractional anisotropy (FA) and mean kurtosis (MK) values were negatively correlated with Hoehn & Yahr (H&Y) scales. In the putamen, FA values was positively correlated with H&Y scales. It is worth noting that, these findings were only observed with the deep-learning method. There was neither group difference, nor correlation with clinical assessments in the SN or striatum exceeding the significant level by using the conventional model fitting method.Conclusions: CNN method improves the robustness of DKI and can help to explore PD-associated imaging features.

scholarly journals Convolutional neural network optimizes the application of diffusion kurtosis imaging in Parkinson’s disease

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Junyan Sun ◽  
Ruike Chen ◽  
Qiqi Tong ◽  
Jinghong Ma ◽  
Linlin Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Model Fitting ◽  
Diffusion Kurtosis Imaging ◽  
Clinical Assessments ◽  
Fitting Method ◽  
Diffusion Kurtosis

Abstract Objectives The literature regarding the use of diffusion-tensor imaging-derived metrics in the evaluation of Parkinson’s disease (PD) is controversial. This study attempted to assess the feasibility of a deep-learning-based method for detecting alterations in diffusion kurtosis measurements associated with PD. Methods A total of 68 patients with PD and 77 healthy controls were scanned using scanner-A (3 T Skyra) (DATASET-1). Meanwhile, an additional five healthy volunteers were scanned with both scanner-A and an additional scanner-B (3 T Prisma) (DATASET-2). Diffusion kurtosis imaging (DKI) of DATASET-2 had an extra b shell compared to DATASET-1. In addition, a 3D-convolutional neural network (CNN) was trained from DATASET-2 to harmonize the quality of scalar measures of scanner-A to a similar level as scanner-B. Whole-brain unpaired t test and Tract-Based Spatial Statistics (TBSS) were performed to validate the differences between the PD and control groups using the model-fitting method and CNN-based method, respectively. We further clarified the correlation between clinical assessments and DKI results. Results An increase in mean diffusivity (MD) was found in the left substantia nigra (SN) in the PD group. In the right SN, fractional anisotropy (FA) and mean kurtosis (MK) values were negatively correlated with Hoehn and Yahr (H&Y) scales. In the putamen (Put), FA values were positively correlated with the H&Y scales. It is worth noting that these findings were only observed with the deep learning method. There was neither a group difference nor a correlation with clinical assessments in the SN or striatum exceeding the significance level using the conventional model-fitting method. Conclusions The CNN-based method improves the robustness of DKI and can help to explore PD-associated imaging features.

scholarly journals Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

2021 ◽  
Vol 13 (3) ◽  
pp. 335
Author(s):  
Yuhao Qing ◽  
Wenyi Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Image Classification ◽  
Deep Network ◽  
Multi Scale

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

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