This　paper　propose　a　retinal　image　segmentation　model　according　to　hybrid　pooling　and　multi-scale　attention　mechanism.　Due　to　the　crucial　importance　of　retinal　vessels　for　the　segmentation　of　the　retina　and　their　intricate　structure,　which　serves　as　vital　diagnostic　information　for　diseases,　we　focus　on　retinal　images　in　medical　imaging.　We　introduce　a　novel　medical　image　segmentation　model,　MDA-Unet,　built　upon　the　Unet　architecture.　Extensive　experiments　are　conducted　on　the　DRIVE　dataset.　Addressing　challenges　such　as　limited　data,　varying　image　quality,　and　the　presence　of　small　structures　in　segmented　target　areas　causing　reduced　segmentation　accuracy,　the　proposed　enhanced　model　MDA-Unet　aims　to　overcome　these　issues.　We　investigate　the　impact　of　incorporating　modules　such　as　Exponential　Moving　Average　(EMA),　Multi-Path　Module　(MPM),　and　serpentine　convolutional　kernels　on　the　model.　The　research　results　indicate　that　the　addition　of　these　modules　enhances　the　ability　of　the　model　to　be　segmented.　This　improved　model　is　tested　with　the　DRIVE　dataset,　yielding　segmentation　results　that　surpass　those　of　previously　proposed　segmentation　models.　Compared　with　the　previous　model　segmentation　results,　the　method　proposed　in　this　paper　has　achieved　good　results　in　fundus　vascular　segmentation.　©　2024　SPIE.