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.　©　2022,　Shanghai　Jiao　Tong　University.