Polyp　segmentation　is　crucial　for　early　detection　of　colorectal　cancer.　With　the　development　of　deep　learning,　training　end-to-end　networks　to　achieve　fully　automatic　polyp　segmentation　has　become　the　mainstream　nowadays.　However,　these　methods　often　face　difficulties　when　dealing　with　out-of-distribution　datasets,　missing　boundaries,　and　small　polyps.　In　2022,　the　introduction　of　FCBFormer　marked　a　significant　advancement　in　vision　tasks,　enhancing　multi-task　computer　vision　performance　while　effectively　tackling　challenges　such　as　out-of-distribution　datasets,　absent　boundaries,　and　small　polyps.　This　innovative　approach　not　only　outperformed　traditional　Vision　Transformer　and　CNN　backbones　but　also　mitigated　their　inherent　limitations.　But　there　are　still　many　areas　for　improvement　in　FCBFormer　architecture　such　as　the　feature　fusion　method,　decoder　architecture　and　the　basic　modules　stacked　by　its　Transformer　branch.　Our　idea　based　on　FCBFormer,　using　the　dual　branch　parallel　network　structure,　proposing　a　more　efficient　feature　fusion　module　named　FCT　and　a　more　reasonable　progressive　decoder　architecture　to　get　the　more　refined　semantic　segmentation　results.　Our　architectture　gained　the　State　of　the　Art　on　the　Kvasir-SEG,　CVC-CLINICDB　dataset,　which　provided　the　effectiveness　of　our　proposed　architecture　for　polyp　segmentation.　©　2024　IEEE.