Region Based Parallel Hierarchy Convolutional Neural Network for Automatic Facial Nerve Paralysis Evaluation

In this article, we propose a parallel hierarchy convolutional neural network (PHCNN) combining a Long Short-Term Memory (LSTM) network structure to quantitatively assess the grading of facial nerve paralysis (FNP) by considering the region-based asymmetric facial features and temporal variation of...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2020-10, Vol.28 (10), p.2325-2332
Main Authors: Liu, Xin, Xia, Yifan, Yu, Hui, Dong, Junyu, Jian, Muwei, Pham, Tuan D.
Format: Article
Language:English
Subjects:
Artificial neural networks
Bells palsy
Diagnostic systems
Evaluation
Face recognition
Facial muscles
Facial nerve
Facial nerve paralysis
Feature extraction
Image sequences
Long short-term memory
LSTM
Muscles
Neural networks
Organs
Paralysis
region of interest
Rehabilitation
severity grade
Shape
spatio-temporal features
Structural hierarchy
Temporal variations
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Summary:In this article, we propose a parallel hierarchy convolutional neural network (PHCNN) combining a Long Short-Term Memory (LSTM) network structure to quantitatively assess the grading of facial nerve paralysis (FNP) by considering the region-based asymmetric facial features and temporal variation of the image sequences. FNP, such as Bell's palsy, is the most common facial symptom of neuromotor dysfunctions. It causes the weakness of facial muscles for the normal emotional expression and movements. The subjective judgement by clinicians completely depends on individual experience, which may not lead to a uniform evaluation. Existing computer-aided methods mainly rely on some complicated imaging equipment, which is complicated and expensive for facial functional rehabilitation. Compared with the subjective judgment and complex imaging processing, the objective and intelligent measurement can potentially avoid this issue. Considering dynamic variation in both global and regional facial areas, the proposed hierarchical network with LSTM structure can effectively improve the diagnostic accuracy and extract paralysis detail from the low-level shape, contour to sematic level features. By segmenting the facial area into two palsy regions, the proposed method can discriminate FNP from normal face accurately and significantly reduce the effect caused by age wrinkles and unrepresentative organs with shape and position variations on feature learning. Experiment on the YouTube Facial Palsy Database and Extended CohnKanade Database shows that the proposed method is superior to the state of the art deep learning methods.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2020.3021410