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Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks

Author

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  • Anping Song

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Zuoyu Wu

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Xuehai Ding

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Qian Hu

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Xinyi Di

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

Abstract

Facial nerve paralysis (FNP) is the most common form of facial nerve damage, which leads to significant physical pain and abnormal function in patients. Traditional FNP detection methods are based on visual diagnosis, which relies solely on the physician’s assessment. The use of objective measurements can reduce the frequency of errors which are caused by subjective methods. Hence, a fast, accurate, and objective computer method for FNP classification is proposed that uses a single Convolutional neural network (CNN), trained end-to-end directly from images, with only pixels and disease labels as inputs. We trained the CNN using a dataset of 1049 clinical images and divided the dataset into 7 categories based on classification standards with the help of neurologists. We tested its performance against the neurologists’ ground truth, and our results matched the neurologists’ level with 97.5% accuracy.

Suggested Citation

  • Anping Song & Zuoyu Wu & Xuehai Ding & Qian Hu & Xinyi Di, 2018. "Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks," Future Internet, MDPI, vol. 10(11), pages 1-13, November.
    • Handle: RePEc:gam:jftint:v:10:y:2018:i:11:p:111-:d:183414
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    References listed on IDEAS

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    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    2. Yann LeCun & Yoshua Bengio & Geoffrey Hinton, 2015. "Deep learning," Nature, Nature, vol. 521(7553), pages 436-444, May.
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Cited by:

    1. Kerang Cao & Jingyu Gao & Kwang-nam Choi & Lini Duan, 2020. "Learning a Hierarchical Global Attention for Image Classification," Future Internet, MDPI, vol. 12(11), pages 1-11, October.

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