Optimization and evaluation of facial recognition models for Williams-Beuren syndrome.

Abstract

The results showed that facial recognition technology can be used to accurately diagnose patients with WBS. Facial recognition models based on VGG-19BN can play a crucial role in its clinical diagnosis. Their performance can be improved by expanding the size of the training dataset, optimizing the CNN architectures applied, and modifying them with a variable cosine learning rate. • The facial gestalt of WBS, often described as "elfin," includes a broad forehead, periorbital puffiness, a flat nasal bridge, full cheeks, and a small chin. • Recent studies have demonstrated the potential of deep convolutional neural networks for facial recognition as a diagnostic tool for WBS. • This study develops six models of facial recognition, EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN, to improve WBS diagnosis. • The VGG-19BN model achieved the best performance, with an accuracy of 95.10% and specificity of 93.42%. The facial recognition model based on VGG-19BN can play a crucial role in the clinical diagnosis of WBS.