Skin　cancer　poses　a　significant　global　health　challenge　due　to　its　increasing　incidence　rates.　Accurate　segmentation　of　skin　lesions　is　essential　for　early　detection　and　successful　treatment,　yet　many　current　techniques　struggle　to　balance　computational　efficiency　with　the　ability　to　capture　complex　lesion　features.　This　paper　aims　to　develop　an　advanced　deep　learning　model　that　improves　segmentation　accuracy　while　maintaining　computational　efficiency,　offering　a　solution　to　the　limitations　of　existing　methods.　We　propose　a　novel　dual-encoder　deep　learning　architecture　incorporating　Squeeze-and-Excitation　(SE)　attention　blocks.　The　model　integrates　two　encoders:　a　pre-trained　ResNet-50　for　extracting　local　features　efficiently　and　a　Vision　Transformer　(ViT)　to　capture　high-level　features　and　long-range　dependencies.　The　fusion　of　these　features,　enhanced　by　SE　attention　blocks,　is　processed　through　a　CNN　decoder,　ensuring　the　model　captures　both　local　and　global　contextual　information.　The　proposed　model　was　evaluated　on　three　benchmark　datasets,　ISIC　2016,　ISIC　2017,　and　ISIC　2018,　achieving　Intersection　over　Union　(IoU)　scores　of　89.53%,　87.02%,　and　84.56%,　respectively.　These　results　highlight　the　model＇s　ability　to　outperform　current　methods　in　balancing　segmentation　accuracy　and　computational　efficiency.　The　findings　demonstrate　that　the　proposed　model　enhances　medical　image　analysis　in　dermatology,　providing　a　promising　tool　for　improving　the　early　detection　of　skin　cancer.