Colorectal　polyps　are　structural　abnormalities　of　the　gastrointestinal　tract　that　can　potentially　become　cancerous　in　some　cases.　The　study　introduces　a　novel　framework　for　colorectal　polyp　segmentation　named　the　Multi-Scale　and　Multi-Path　Cascaded　Convolution　Network　(MMCC-Net),　aimed　at　addressing　the　limitations　of　existing　models,　such　as　inadequate　spatial　dependence　representation　and　the　absence　of　multi-level　feature　integration　during　the　decoding　stage　by　integrating　multi-scale　and　multi-path　cascaded　convolutional　techniques　and　enhances　feature　aggregation　through　dual　attention　modules,　skip　connections,　and　a　feature　enhancer.　MMCCNet　achieves　superior　performance　in　identifying　polyp　areas　at　the　pixel　level.　The　Proposed　MMCC-Net　was　tested　across　six　public　datasets　and　compared　against　eight　SOTA　models　to　demonstrate　its　efficiency　in　polyp　segmentation.　The　MMCC-Net＇s　performance　shows　Dice　scores　with　confidence　interval　ranging　between　77.43　+/-　0.12,　(77.08,　77.56)　and　94.45　+/-　0.12,　(94.19,　94.71)　and　Mean　Intersection　over　Union　(MIoU)　scores　with　confidence　interval　ranging　from　72.71　+/-　0.19,　(72.20,　73.00)　to　90.16　+/-　0.16,　(89.69,　90.53)　on　the　six　databases.　These　results　highlight　the　model＇s　potential　as　a　powerful　tool　for　accurate　and　efficient　polyp　segmentation,　contributing　to　early　detection　and　prevention　strategies　in　colorectal　cancer.