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首页> 外文期刊>Frontiers in Pediatrics >Automatic Facial Recognition of Williams-Beuren Syndrome Based on Deep Convolutional Neural Networks
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Automatic Facial Recognition of Williams-Beuren Syndrome Based on Deep Convolutional Neural Networks

机译：基于深卷积神经网络的威廉姆斯 - 贝仑综合征的自动面部识别

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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.
机译：背景：Williams-Beuren综合征（WBS）是一种罕见的遗传综合征，具有特征“Elfin”面部甲茅。 “Elfin”面部特性包括广泛的额头，眶型浮肿，扁平鼻桥，短嘴鼻，宽口，厚嘴唇和尖球。最近，深卷积神经网络（CNNS）已成功应用于诊断遗传综合征的面部识别。然而，使用深CNNS的WBS面部识别几乎没有研究。目的：本研究的目的是基于深CNN的WBS诊断构建自动面部识别模型。方法：该研究招收了104个WBS儿童，91例患者其他遗传综合征，145例健康儿童。照片数据集仅使用来自每个参与者的一个正面面部照片。通过采用VGG-16，VGG-19，Reset-18，Reset-34和MobileNet-V2架构，构建WBS的五个面部识别框架。 Imagenet转移学习用于避免过度拟合。面部识别模型的分类性能由五倍交叉验证评估，并进行与人体专家的比较。结果：构建了WBS的五个面部识别框架。 VGG-19型号实现了最佳性能。 VGG-19型号的准确度，精度，召回，F1分数和面积（AUC）的曲线（AUC）为92.7±1.3％，94.0±5.6％，81.7±3.6％，87.2±2.0％和89.6±1.3％，分别。人类专家的最高准确性，精确度，召回，F1分数和AUC分别为82.1,65.9,85.6,74.5和83.0％。每个人类专家的AUC均不如VGG-16的AUC（88.6±3.5％），VGG-19（89.6±1.3％），RESET-18（83.6±8.2％）和Resnet-34（86.3± 4.9％）模型。结论：本研究强调了在临床实践中使用深CNN诊断WBS的可能性。基于VGG-19的面部识别框架可以在WBS诊断中发挥着突出的作用。转移学习技术可以帮助构建具有小规模数据集的遗传综合征的面部识别模型。

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