Multiple Genetic Syndromes Recognition Based on a Deep Learning Framework and Cross-Loss Training

Many patients with genetic syndromes have special facial features, which boast significant potential value for clinical diagnosis. Deep learning and computer vision technology can be employed to diagnose genetic diseases by analyzing facial features of patients. As a matter of fact, the application of deep learning technology in the area of genetic diseases is confined owing to the difficulties of patient data acquisition. This study develops BioFace, a deep learning framework that can recognize multiple genetic diseases facial attributes based on limited datasets. BioFace is a deep neural network architecture designed premised on Resnet. To increase the weight of effective features and weaken the weight of invalid or unobvious features during extraction of facial features, we add Squeeze-and-Excitation (SE) blocks in the network. In combination with this network architecture, we designed a cross-loss training method based on transfer learning. This method can transfer the ability learned from the task of face identification to the task of recognition of genetic diseases facial attribute, and improve the inter-class distance of different genetic diseases and the intra-class distance of similar genetic diseases simultaneously. These render it possible for deep learning to be applied to recognition of multiple genetic diseases facial attribute with very small amount of data. In this research, we tested 10 syndromes with our framework and the Top-1 accuracy was 93.5%, which is the state-of-the-art in multiple genetic syndromes recognition research. In practical clinical applications, our framework and methods can be extended to the disease identification of more small datasets, potentially offering valuable assistance for the auxiliary clinical application of genetic diagnosis and other related genetic research.