Addressing　the　challenges　posed　by　colorectal　polyp　variability　and　imaging　inconsistencies　in　endoscopic　images,　we　propose　the　multiscale　feature　fusion　booster　network　(MFFB-Net),　a　novel　deep　learning　(DL)　framework　for　the　semantic　segmentation　of　colorectal　polyps　to　aid　in　early　colorectal　cancer　detection.　Unlike　prior　models,　such　as　the　pyramid　vision　transformer-based　cascaded　attention　decoder　(PVT-CASCADE)　and　the　parallel　reverse　attention　network　(PraNet),　MFFB-Net　enhances　segmentation　accuracy　and　efficiency　through　a　unique　fusion　of　multiscale　feature　extraction　in　both　the　encoder　and　decoder　stages,　coupled　with　a　booster　module　for　refining　fine-grained　details　and　a　bottleneck　module　for　efficient　feature　compression.　The　network　leverages　multipath　feature　extraction　with　skip　connections,　capturing　both　local　and　global　contextual　information,　and　is　rigorously　evaluated　on　seven　benchmark　datasets,　including　Kvasir,　CVC-ClinicDB,　CVC-ColonDB,　ETIS,　CVC-300,　BKAI-IGH,　and　EndoCV2020.　MFFB-Net　achieves　state-of-the-art　(SOTA)　performance,　with　Dice　scores　of　94.38%,　91.92%,　91.21%,　80.34%,　82.67%,　76.92%,　and　74.29%　on　CVC-ClinicDB,　Kvasir,　CVC-300,　ETIS,　CVC-ColonDB,　EndoCV2020,　and　BKAI-IGH,　respectively,　outperforming　existing　models　in　segmentation　accuracy　and　computational　efficiency.　MFFB-Net　achieves　real-time　processing　speeds　of　26　FPS　with　only　1.41　million　parameters,　making　it　well　suited　for　real-world　clinical　applications.　The　results　underscore　the　robustness　of　MFFB-Net,　demonstrating　its　potential　for　real-time　deployment　in　computer-aided　diagnosis　systems　and　setting　a　new　benchmark　for　automated　polyp　segmentation.