The　automatic　detection　of　skin　diseases　via　dermoscopic　images　can　improve　the　efficiency　in　diagnosis　and　help　doctors　make　more　accurate　judgments.　However,　conventional　skin　disease　recognition　systems　may　produce　high　confidence　for　out-of-distribution　(OOD)　data,　which　may　become　a　major　security　vulnerability　in　practical　applications.　In　this　paper,　we　propose　a　multi-scale　detection　framework　to　detect　out-of-distribution　skin　disease　image　data　to　ensure　the　robustness　of　the　system.　Our　framework　extracts　features　from　different　layers　of　the　neural　network.　In　the　early　layers,　rectified　activation　is　used　to　make　the　output　features　closer　to　the　well-behaved　distribution,　and　then　an　one-class　SVM　is　trained　to　detect　OOD　data;　in　the　penultimate　layer,　an　adapted　Gram　matrix　is　used　to　calculate　the　features　after　rectified　activation,　and　finally　the　layer　with　the　best　performance　is　chosen　to　compute　a　normality　score.　Experiments　show　that　the　proposed　framework　achieves　superior　performance　when　compared　with　other　state-of-the-art　methods　in　the　task　of　skin　disease　recognition.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.