In　ear　of　smart　cities,　intelligent　medical　image　recognition　technique　has　become　a　promising　way　to　solve　remote　patient　diagnosis　in　IoMT.　Although　deep　learning-based　recognition　approaches　have　received　great　development　during　the　past　decade,　explainability　always　acts　as　a　main　obstacle　to　promote　recognition　approaches　to　higher　levels.　Because　it　is　always　hard　to　clearly　grasp　internal　principles　of　deep　learning　models.　In　contrast,　the　conventional　machine　learning　(CML)-based　methods　are　well　explainable,　as　they　give　relatively　certain　meanings　to　parameters.　Motivated　by　the　above　view,　this　paper　combines　deep　learning　with　the　CML,　and　proposes　a　hybrid　intelligence-driven　medical　image　recognition　framework　in　IoMT.　On　the　one　hand,　the　convolution　neural　network　is　utilized　to　extract　deep　and　abstract　features　for　initial　images.　On　the　other　hand,　the　CML-based　techniques　are　employed　to　reduce　dimensions　for　extracted　features　and　construct　a　strong　classifier　that　output　recognition　results.　A　real　dataset　about　pathologic　myopia　is　selected　to　establish　simulative　scenario,　in　order　to　assess　the　proposed　recognition　framework.　Results　reveal　that　the　proposal　that　improves　recognition　accuracy　about　two　to　three　percent.