Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks

Anping Song, Zuoyu Wu, Xuehai Ding, Qian Hu and Xinyi Di
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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

Future Internet, 2018, vol. 10, issue 11, 1-13

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.

Keywords: facial image analysis; facial nerve paralysis; deep convolutional neural networks; image classification (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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