Back to Search Start Over

Automatic Facial Recognition of Williams-Beuren Syndrome Based on Deep Convolutional Neural Networks

Authors :
Hui Liu
Zi-Hua Mo
Hang Yang
Zheng-Fu Zhang
Dian Hong
Long Wen
Min-Yin Lin
Ying-Yi Zheng
Zhi-Wei Zhang
Xiao-Wei Xu
Jian Zhuang
Shu-Shui Wang
Source :
Frontiers in Pediatrics, Vol 9 (2021)
Publication Year :
2021
Publisher :
Frontiers Media S.A., 2021.

Abstract

Background: Williams-Beuren syndrome (WBS) is a rare genetic syndrome with a characteristic “elfin” facial gestalt. The “elfin” facial characteristics include a broad forehead, periorbital puffiness, flat nasal bridge, short upturned nose, wide mouth, thick lips, and pointed chin. Recently, deep convolutional neural networks (CNNs) have been successfully applied to facial recognition for diagnosing genetic syndromes. However, there is little research on WBS facial recognition using deep CNNs.Objective: The purpose of this study was to construct an automatic facial recognition model for WBS diagnosis based on deep CNNs.Methods: The study enrolled 104 WBS children, 91 cases with other genetic syndromes, and 145 healthy children. The photo dataset used only one frontal facial photo from each participant. Five face recognition frameworks for WBS were constructed by adopting the VGG-16, VGG-19, ResNet-18, ResNet-34, and MobileNet-V2 architectures, respectively. ImageNet transfer learning was used to avoid over-fitting. The classification performance of the facial recognition models was assessed by five-fold cross validation, and comparison with human experts was performed.Results: The five face recognition frameworks for WBS were constructed. The VGG-19 model achieved the best performance. The accuracy, precision, recall, F1 score, and area under curve (AUC) of the VGG-19 model were 92.7 ± 1.3%, 94.0 ± 5.6%, 81.7 ± 3.6%, 87.2 ± 2.0%, and 89.6 ± 1.3%, respectively. The highest accuracy, precision, recall, F1 score, and AUC of human experts were 82.1, 65.9, 85.6, 74.5, and 83.0%, respectively. The AUCs of each human expert were inferior to the AUCs of the VGG-16 (88.6 ± 3.5%), VGG-19 (89.6 ± 1.3%), ResNet-18 (83.6 ± 8.2%), and ResNet-34 (86.3 ± 4.9%) models.Conclusions: This study highlighted the possibility of using deep CNNs for diagnosing WBS in clinical practice. The facial recognition framework based on VGG-19 could play a prominent role in WBS diagnosis. Transfer learning technology can help to construct facial recognition models of genetic syndromes with small-scale datasets.

Details

Language :
English
ISSN :
22962360
Volume :
9
Database :
Directory of Open Access Journals
Journal :
Frontiers in Pediatrics
Publication Type :
Academic Journal
Accession number :
edsdoj.0376f2cdc60040f8bf030bdaff8f4c45
Document Type :
article
Full Text :
https://doi.org/10.3389/fped.2021.648255