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To propose a more generalized severity evaluation model, this paper proposes an explainable 3D multi-head attention residual convolution network.
The model was trained at different temporal resolutions, achieving the highest accuracy of 91% with input sequences of 14 frames.
To propose a more generalized severity evaluation model, this paper proposes an explainable 3D multi-head attention residual convolution network. First, we ...
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In this study, we propose a machine learning model that accurately classifies any given DaTSCAN as having Parkinson's disease or not.
Sep 9, 2024 · This study has demonstrated the potential of multimodal deep learning approaches in detecting Parkinson's Disease (PD) at its prodromal stage.
An automated detection system for Parkinson's disease (PD) employing the convolutional neural network (CNN) is proposed in this study.
In this systematic review, we aim to (a) comprehensively sum- marize published studies that applied advanced DL models to the diagnosis of PD-related symptoms ...
In this study, we propose a machine learning model that accurately classifies any given DaTSCAN as having Parkinson's disease or not, in addition to providing a ...
Parkinson's severity diagnosis explainable model based on 3D multi-head attention residual network. Jiehui Huang, Lishan Lin, Fengcheng Yu, Xuedong He ...
Jul 10, 2020 · Parkinson's severity diagnosis explainable model based on 3D multi-head attention residual network. Computers in Biology and Medicine 2024 ...