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Wei-Chang Du
Researcher at I-Shou University
Publications - 10
Citations - 64
Wei-Chang Du is an academic researcher from I-Shou University. The author has contributed to research in topics: Ultrasound & Spect imaging. The author has an hindex of 3, co-authored 10 publications receiving 25 citations.
Papers
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Journal ArticleDOI
Feasible Classified Models for Parkinson Disease from 99mTc-TRODAT-1 SPECT Imaging.
Shih-Yen Hsu,Hsin-Chieh Lin,Tai-Been Chen,Wei-Chang Du,Yun-Hsuan Hsu,Yi-Chen Wu,Po-Wei Tu,Yung-Hui Huang,Huei-Yung Chen +8 more
TL;DR: The results showed that the SVM-based model could provide further reference for PD stage classification in medical diagnosis and claimed that the false positive rate in this classification model could be clarified.
Journal ArticleDOI
Classification of the Multiple Stages of Parkinson's Disease by a Deep Convolution Neural Network Based on 99mTc-TRODAT-1 SPECT Images.
Shih-Yen Hsu,Li-Ren Yeh,Tai-Been Chen,Wei-Chang Du,Yung-Hui Huang,Wen-Hung Twan,Ming-Chia Lin,Yun-Hsuan Hsu,Yi-Chen Wu,Huei-Yung Chen +9 more
TL;DR: Deep learning methods are utilizes to establish a multiple stages classification model of Parkinson’s disease and the best accuracy of the models based on grayscale and color images in four and six stages were 0.83 (AlexNet), 0.85 (VGG),0.78 (DenseNet) and 0.78(Dense net).
Journal ArticleDOI
Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks
Li-Ren Yeh,Wei-Chin Chen,Hua-Yan Chan,Nan-Han Lu,Chi-Yuan Wang,Wen-Hung Twan,Wei-Chang Du,Yung-Hui Huang,Shih-Yen Hsu,Tai-Been Chen +9 more
TL;DR: Wang et al. as mentioned in this paper used convolutional neural networks (CNNs) to classify ECG image types to assist in anesthesia assessment, and showed that it is feasible to measure ECG in real time through IoT and then distinguish four types through CNNs.
Journal ArticleDOI
Integrate Weather Radar and Monitoring Devices for Urban Flooding Surveillance.
TL;DR: This study develops ARMT, which integrates real-time ground radar echo images and CCTV images and automatically estimates a rainfall hotspot according to the cloud intensity, to help decision makers better understand the on-site situation and make an evacuation decision before the flood disaster occurs.
Journal ArticleDOI
Classification for liver ultrasound tomography by posterior attenuation correction with a phantom study
Chih I. Chen,Tai-Been Chen,Nan-Han Lu,Wei-Chang Du,Chih-Yu Liang,Ko-Ing Liu,Shih-Yen Hsu,Li-Wei Lin,Yung-Hui Huang +8 more
TL;DR: The hybrid method has been proven to be more accurate and have better performance and less error than either single method and the deep learning approaches may be considered for the application in classifying liver ultrasound images.