Publikation: Deep assessment process : Objective assessment process for unilateral peripheral facial paralysis via deep convolutional neural network
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2017
Autor:innen
Guo, Zhe-Xiao
Zhou, Yongjin
Xiang, Jianghuai
Ding, Huijun
Chen, Shifeng
Dan, Guo
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2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) : From Nano to Macro. Piscataway, NJ: IEEE, 2017, pp. 135-138. eISSN 1945-8452. ISBN 978-1-5090-1172-8. Available under: doi: 10.1109/ISBI.2017.7950486
Zusammenfassung
Unilateral peripheral facial paralysis (UPFP) is a form of facial nerve paralysis and clinically classified according to facial asymmetry. Prompt and precise assessment is crucial to the neural rehabilitation of UPFP. For UPFP assessment, most of the existing assessment systems are subjective and empirical. Therefore, an objective assessment system will help clinical doctors to obtain a prompt and precise assessment. Distinguishing precisely between degrees of asymmetry is hard using pure pattern recognition methods. Thus, a novel objective assessment process based on convolutional neuronal networks is proposed in this paper that provides an end-to-end solution. This method could alleviate the problem and produced a classification accuracy of 91.25% for predicting the House-Brackmann degree on a given UPFP image dataset.
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Unilateral peripheral facial paralysis, objective assessment process, deep convolutional neural network, House-Brackmann facial nerve grading system
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2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 18. Apr. 2017 - 21. Apr. 2017, Melbourne, Australia
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GUO, Zhe-Xiao, Minmin SHEN, Le DUAN, Yongjin ZHOU, Jianghuai XIANG, Huijun DING, Shifeng CHEN, Oliver DEUSSEN, Guo DAN, 2017. Deep assessment process : Objective assessment process for unilateral peripheral facial paralysis via deep convolutional neural network. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, Australia, 18. Apr. 2017 - 21. Apr. 2017. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) : From Nano to Macro. Piscataway, NJ: IEEE, 2017, pp. 135-138. eISSN 1945-8452. ISBN 978-1-5090-1172-8. Available under: doi: 10.1109/ISBI.2017.7950486BibTex
@inproceedings{Guo2017-04asses-39638,
year={2017},
doi={10.1109/ISBI.2017.7950486},
title={Deep assessment process : Objective assessment process for unilateral peripheral facial paralysis via deep convolutional neural network},
isbn={978-1-5090-1172-8},
publisher={IEEE},
address={Piscataway, NJ},
booktitle={2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) : From Nano to Macro},
pages={135--138},
author={Guo, Zhe-Xiao and Shen, Minmin and Duan, Le and Zhou, Yongjin and Xiang, Jianghuai and Ding, Huijun and Chen, Shifeng and Deussen, Oliver and Dan, Guo}
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<dcterms:abstract xml:lang="eng">Unilateral peripheral facial paralysis (UPFP) is a form of facial nerve paralysis and clinically classified according to facial asymmetry. Prompt and precise assessment is crucial to the neural rehabilitation of UPFP. For UPFP assessment, most of the existing assessment systems are subjective and empirical. Therefore, an objective assessment system will help clinical doctors to obtain a prompt and precise assessment. Distinguishing precisely between degrees of asymmetry is hard using pure pattern recognition methods. Thus, a novel objective assessment process based on convolutional neuronal networks is proposed in this paper that provides an end-to-end solution. This method could alleviate the problem and produced a classification accuracy of 91.25% for predicting the House-Brackmann degree on a given UPFP image dataset.</dcterms:abstract>
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