摘要Turner syndrome(TS)is a chromosomal disorder disease that only affects the growth of female patients.Prompt diagnosis is of high significance for the patients.However,clinical screening methods are time-consuming and cost-expensive.Some researchers used machine learning-based methods to detect TS,the performance of which needed to be improved.Therefore,we propose an ensemble method of two-path capsule networks(CapsNets)for detecting TS based on global-local facial images.Specifically,the TS facial images are preprocessed and segmented into eight local parts under the direction of physicians;then,nine two-path CapsNets are respectively trained using the complete TS facial images and eight local images,in which the few-shot learning is utilized to solve the problem of limited data;finally,a probability-based ensemble method is exploited to combine nine classifiers for the classification of TS.By studying base classifiers,we find two meaningful facial areas are more related to TS patients,i.e.,the parts of eyes and nose.The results demonstrate that the proposed model is effective for the TS classification task,which achieves the highest accuracy of 0.924 1.