The　accurate　segmentation　of　breast　tumours　is　important　for　the　diagnosis　and　treatment　of　breast　cancer.　When　using　the　classic　U-Net,　Attention-UNet,　and　UNet++　to　segment　the　tumour,　there　are　problems　of　oversegmentation,　incorrect　segmentation,　and　poor　edge　continuity.　In　this　paper,　an　effective　tumour　segmentation　method,　EfficientUNet,　is　proposed.　EfficientUNet　adopts　a　step-by-step　enhancement　method,　combining　ResNet18,　a　channel　attention　mechanism　and　deep　supervision.　ResNet18,　as　the　encoder　of　the　whole　network,　solves　the　problem　of　gradient　disappearanceand　improves　the　feature　extraction　ability　of　the　model.　The　channel　attention　module　makes　the　model　more　accurate　in　tumour　edge　processing.　The　deep　supervision　technology　accelerates　the　model　training　and　provides　the　convergence　direction　for　the　model.　In　addition,　it　is　found　that　when　adjusting　the　size　of　the　image,　the　method　of　image　filling　before　clipping　(or　zooming)　is　more　conducive　to　model　learning　than　the　direct　interpolation　method.　And　a　comparative　experiment　wasperformed　on　dataset　B.　Compared　to　U-Net,　Attention-UNet　and　UNet++,　EfficientUNet　has　the　highest　performance.　Finally,　the　ablation　experiment　also　indicated　the　effectiveness　of　each　module　in　EfficientUNet.　An　effective　tumour　segmentation　method,　EfficientUNet,　for　breast　ultrasound　images,　is　proposed.　EfficientUNet　adopts　a　step-by-step　enhancement　method,　combining　ResNet18,　channel　attention　mechanism,　and　deep　supervision.　ResNet18,　as　the　encoder　of　the　whole　network,　solves　the　problem　of　gradient　disappearance　caused　by　the　network　being　too　deep　to　improve　the　feature　extraction　ability　of　the　model.　The　channel　attention　module　improves　the　segmentation　performance　of　the　whole　model　effectively,　and　makes　the　model　more　accurate　in　tumour　edge　processing.image