Sherif Fayroz F, Tawfik Nahed, Mousa Doaa, Abdallah Mohamed S, Cho Young-Im
Computers and Systems Department, Electronics Research Institute (ERI), Cairo 11843, Egypt.
Informatics Department, Electronics Research Institute (ERI), Cairo 11843, Egypt.
Bioengineering (Basel). 2024 Aug 13;11(8):827. doi: 10.3390/bioengineering11080827.
Genetic disorders affect over 6% of the global population and pose substantial obstacles to healthcare systems. Early identification of these rare facial genetic disorders is essential for managing related medical complexities and health issues. Many people consider the existing screening techniques inadequate, often leading to a diagnosis several years after birth. This study evaluated the efficacy of deep learning-based classifier models for accurately recognizing dysmorphic characteristics using facial photos. This study proposes a multi-class facial syndrome classification framework that encompasses a unique combination of diseases not previously examined together. The study focused on distinguishing between individuals with four specific genetic disorders (Down syndrome, Noonan syndrome, Turner syndrome, and Williams syndrome) and healthy controls. We investigated how well fine-tuning a few well-known convolutional neural network (CNN)-based pre-trained models-including VGG16, ResNet-50, ResNet152, and VGG-Face-worked for the multi-class facial syndrome classification task. We obtained the most encouraging results by adjusting the VGG-Face model. The proposed fine-tuned VGG-Face model not only demonstrated the best performance in this study, but it also performed better than other state-of-the-art pre-trained CNN models for the multi-class facial syndrome classification task. The fine-tuned model achieved both accuracy and an F1-Score of 90%, indicating significant progress in accurately detecting the specified genetic disorders.
遗传疾病影响着全球超过6%的人口，给医疗保健系统带来了巨大障碍。尽早识别这些罕见的面部遗传疾病对于应对相关的医疗复杂性和健康问题至关重要。许多人认为现有的筛查技术不够完善，常常导致在出生几年后才得以诊断。本研究评估了基于深度学习的分类器模型使用面部照片准确识别畸形特征的效果。本研究提出了一个多类面部综合征分类框架，该框架包含了以前未一起研究过的疾病的独特组合。该研究专注于区分患有四种特定遗传疾病（唐氏综合征、努南综合征、特纳综合征和威廉姆斯综合征）的个体与健康对照。我们研究了对一些著名的基于卷积神经网络（CNN）的预训练模型（包括VGG16、ResNet-50、ResNet152和VGG-Face）进行微调在多类面部综合征分类任务中的效果。通过调整VGG-Face模型，我们获得了最令人鼓舞的结果。所提出的微调VGG-Face模型不仅在本研究中表现出最佳性能，而且在多类面部综合征分类任务中也比其他先进的预训练CNN模型表现更好。微调后的模型准确率和F1分数均达到90%，表明在准确检测特定遗传疾病方面取得了显著进展。
