The　lesion　area　segmentation　technology　in　endoscopic　images　helps　doctors　discover　and　locate　lesion　areas,　which　is　crucial　for　improving　the　survival　rate　of　patients.　In　various　tasks　within　the　field　of　medical　image　segmentation,　the　fully　convolutional　network　with　U-shaped　architecture　has　achieved　great　success,　but　the　fully　convolutional　operation　has　limitations　in　obtaining　global　context　information.The　Segment　Anything　Model　(SAM),　the　latest　foundational　model　in　computer　vision,　boasts　powerful　image　feature　extraction　capabilities　and　can　capture　explicit　long-range　dependencies,　but　it　cannot　capture　low-level　detailed　information.　This　paper　proposes　a　dual-branch　model,　named　FCSAM,　which　makes　up　for　the　respective　shortcomings　of　the　fully　convolutional　network　and　SAM　in　medical　image　segmentation.　At　the　same　time,　this　paper　proposes　a　LayerNorm　and　low-rank-based(LNLoRA)　fine-tuning　strategy,　which　overcomes　the　problem　that　large　models　cannot　be　applied　to　small-scale　medical　image　datasets　and　reduces　the　deployment　and　storage　overhead　of　the　model.　In　addition,　we　also　design　a　boundary　aware　module　and　a　boundary　key　point　map　generation　algorithm　to　improve　the　boundary　localization　ability　of　this　model,　and　significantly　improve　the　segmentation　performance　of　lesions.　The　experimental　results　show　that　the　IOU,　Dice　coefficient　and　accuracy　of　the　model　are　79.3%,　88.1%　and　93.2%　respectively.　These　metrics　show　that　the　model　proposed　in　this　paper　achieves　superior　performance　to　various　competing　methods　in　terms　of　segmentation　accuracy　and　has　great　potential　in　clinical　analysis　and　diagnosis.