Gastroscopy　is　an　important　step　in　the　diagnosis　of　early　gastric　cancer.　However,　because　the　morphological　manifestations　of　early　gastric　cancer　are　not　obvious,　endoscopists　need　long-term　specialized　training　and　experience　accumulation　to　correctly　identify　early　cancer　through　magnification　gastroscopy.　In　this　paper,　the　data　set　of　gastroscopy　image　is　collected　and　enhanced,　and　target　detection　method　is　combined　with　gastroscopy　image.　The　Mask　R-CNN+BiFPN　model　was　proposed　to　enhance　the　feature　fusion　and　improve　the　detection　effect　of　early　gastric　cancer　lesions.　Compared　with　Mask　R-CNN,　the　improved　Mask　R-CNN　model　has　better　performance,　with　the　sensitivity　and　specificity　of　91.67%　and　88.95%　in　accurately　labeled　gastroscopic　datasets,　respectively,　showing　a　good　segmentation　effect　for　surface　swelling　lesions.