Deep assessment process: Objective assessment process for unilateral peripheral facial paralysis via deep convolutional neural network

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.

[1]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Ying Li,et al.  Current situation and evaluation of clinical studies on acupuncture and moxibustion treatment of peripheral facial paralysis at selected stages. , 2010, Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan.

[3]  Yann LeCun,et al.  Convolutional networks and applications in vision , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[4]  Kenneth Sundaraj,et al.  Initial assessment of facial nerve paralysis based on motion analysis using an optical flow method. , 2016, Technology and health care : official journal of the European Society for Engineering and Medicine.

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Yaozong Gao,et al.  Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching , 2017, Deep Learning for Medical Image Analysis.

[7]  J. Finsterer Management of peripheral facial nerve palsy , 2008, European Archives of Oto-Rhino-Laryngology.

[8]  Hayit Greenspan,et al.  Chest pathology detection using deep learning with non-medical training , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[9]  John J. Soraghan,et al.  Quantitative Analysis of Facial Paralysis Using Local Binary Patterns in Biomedical Videos , 2009, IEEE Transactions on Biomedical Engineering.

[10]  Ting Wang,et al.  Automatic evaluation of the degree of facial nerve paralysis , 2016, Multimedia Tools and Applications.

[11]  Junyu Dong,et al.  Evaluation of Facial Paralysis Degree Based on Regions , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[12]  P Dulguerov,et al.  Review of objective topographic facial nerve evaluation methods. , 1999, The American journal of otology.

[13]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[14]  J. W. House,et al.  Facial Nerve Grading System , 1985, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[15]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.