In　medical　imaging,　efficient　segmentation　of　colon　polyps　plays　a　pivotal　role　in　minimally　invasive　solutions　for　colorectal　cancer.　This　study　introduces　a　novel　approach　employing　two　parallel　encoder　branches　within　a　network　for　polyp　segmentation.　One　branch　of　the　encoder　incorporates　the　dual　convolution　blocks　that　have　the　capability　to　maintain　feature　information　over　increased　depths,　and　the　other　block　embraces　the　single　convolution　block　with　the　addition　of　the　previous　layer＇s　feature,　offering　diversity　in　feature　extraction　within　the　encoder,　combining　them　before　transpose　layers　with　a　depth-wise　concatenation　operation.　Our　model　demonstrated　superior　performance,　surpassing　several　established　deep-learning　architectures　on　the　Kvasir　and　CVC-ClinicDB　datasets,　achieved　a　Dice　score　of　0.919,　a　mIoU　of　0.866　for　the　Kvasir　dataset,　and　a　Dice　score　of　0.931　and　a　mIoU　of　0.891　for　the　CVC-ClinicDB.　The　visual　and　quantitative　results　highlight　the　efficacy　of　our　model,　potentially　setting　a　new　model　in　medical　image　segmentation.