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Your search keyword '"Huang, Chin-Yin"' showing total 8 results
Start Over Author "Huang, Chin-Yin" Topic deep learning
8 results on '"Huang, Chin-Yin"'

1. Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis.

Author

Tsou CH, Cheng YC, Huang CY, Chen JH, Chen WH, Chai JW, and Chen CC

Subjects
Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Cross-Sectional Studies, Female, Humans, Hydrocephalus, Normal Pressure diagnostic imaging, Infant, Infant, Newborn, Male, Middle Aged, Retrospective Studies, Young Adult, Cerebral Aqueduct diagnostic imaging, Deep Learning, Hydrocephalus, Normal Pressure cerebrospinal fluid, Magnetic Resonance Imaging methods, Neural Networks, Computer
Abstract

Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aqueduct has been utilized for diagnosis of conditions such as normal pressure hydrocephalus (NPH). This study aims to develop an automated method of aqueduct CSF flow analysis using convolution neural networks (CNNs), which can replace the current standard involving manual segmentation of aqueduct region of interest (ROI). Retrospective analysis was performed on 333 patients who underwent PC-MRI, totaling 353 imaging studies. Aqueduct flow measurements using manual ROI placement was performed independently by two radiologists. Two types of CNNs, MultiResUNet and UNet, were trained using ROI data from the senior radiologist, with PC-MRI studies being randomly divided into training (80%) and validation (20%) datasets. Segmentation performance was assessed using Dice similarity coefficient (DSC), and CSF flow parameters were calculated from both manual and CNN-derived ROIs. MultiResUNet, UNet and second radiologist (Rater 2) had DSCs of 0.933, 0.928, and 0.867, respectively, with p < 0.001 between CNNs and Rater 2. Comparison of CSF flow parameters showed excellent intraclass correlation coefficients (ICCs) for MultiResUNet, with lowest correlation being 0.67. For UNet, lower ICCs of -0.01 to 0.56 were observed. Only 3/353 (0.8%) studies failed to have appropriate ROIs placed by MultiResUNet, compared to 12/353 (3.4%) failed cases for UNet. In conclusion, CNNs were able to measure aqueductal CSF flow with similar performance to a senior neuroradiologist. MultiResUNet demonstrated fewer cases of segmentation failure and more consistent flow measurements compared to the widely adopted UNet., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Published
2021
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2. Face Emotion Expression Recognition Using DLIB Model and Convolutional Neural Network Approach for Supporting Online Learning

Author

Wiryasaputra, Rita, Huang, Chin-Yin, Juliansyah, Jimmy, Yang, Chao-Tung, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Deng, Der-Jiunn, editor, Chao, Han-Chieh, editor, and Chen, Jyh-Cheng, editor

Published
2023
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3. The Implementation of an Edge Computing Architecture with LoRaWAN for Air Quality Monitoring Applications

Author

Kristiani, Endah, Yang, Chao-Tung, Huang, Chin-Yin, Ko, Po-Cheng, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Deng, Der-Jiunn, editor, Pang, Ai-Chun, editor, and Lin, Chun-Cheng, editor

Published
2020
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4. Image Classification Model Using Deep Learning on the Edge Device

Author

Kristiani, Endah, Yang, Chao-Tung, Huang, Chin-Yin, Chan, Yu-Wei, Fathoni, Halim, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Yang, Chao-Tung, editor, Pei, Yan, editor, and Chang, Jia-Wei, editor

Published
2020
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5. PM2.5 Forecasting Using LSTM Sequence to Sequence Model in Taichung City

Author

Kristiani, Endah, Yang, Chao-Tung, Huang, Chin-Yin, Lin, Jwu-Rong, Nguyen, Kieu Lan Phuong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Kim, Kuinam J., editor, and Kim, Hye-Young, editor

Published
2020
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7. Short-Term Prediction of PM 2.5 Using LSTM Deep Learning Methods.

Author

Kristiani, Endah, Lin, Hao, Lin, Jwu-Rong, Chuang, Yen-Hsun, Huang, Chin-Yin, and Yang, Chao-Tung

Abstract

This paper implements deep learning methods of recurrent neural networks and short-term memory models. Two kinds of time-series data were used: air pollutant factors, such as O3, SO2, and CO2 from 2017 to 2019, and meteorological factors such as temperature, humidity, wind direction, and wind speed. A trained model was used to predict air pollution within an eight-hour period. Correlation analysis was applied using Pearson and Spearman correlation coefficients. The KNN method was used to fill in the missing values to improve the generated model's accuracy. The average absolute error percentage value was used in the experiments to evaluate the model's performance. LSTM had the lowest RMSE value at 1.9 than the other models from the experiments. CNN had a significant RMSE value at 3.5, followed by Bi-LSTM at 2.5 and Bi-GRU at 2.7. In comparison, the RNN was slightly higher than LSTM at a 2.4 value. [ABSTRACT FROM AUTHOR]

Published
2022
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8. Electrode Tab Deflection Detection for Pouch Lithium-Ion Battery Using Mask R-CNN

Author

Lin, Yih-Lon, Chiang, Yu-Min, Liu, Chia-Ming, Huang, Sih-Wei, Behrens, Bernd-Arno, Series Editor, Grzesik, Wit, Series Editor, Ihlenfeldt, Steffen, Series Editor, Kara, Sami, Series Editor, Ong, Soh-Khim, Series Editor, Tomiyama, Tetsuo, Series Editor, Williams, David, Series Editor, Huang, Chin-Yin, editor, Dekkers, Rob, editor, Chiu, Shun Fung, editor, Popescu, Daniela, editor, and Quezada, Luis, editor

Published
2023
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