Skin　cancer　is　a　prevalent　health　concern,　and　accurate　segmentation　of　skin　lesions　is　crucial　for　early　diagnosis.　Existing　methods　for　skin　lesion　segmentation　often　face　trade-offs　between　efficiency　and　feature　extraction　capabilities.　This　paper　proposes　Dual　Skin　Segmentation　(DuaSkinSeg),　a　deep-learning　model,　to　address　this　gap　by　utilizing　dual　encoders　for　improved　performance.　DuaSkinSeg　leverages　a　pre-trained　MobileNetV2　for　efficient　local　feature　extraction.　Subsequently,　a　Vision　Transformer-Convolutional　Neural　Network　(ViT-CNN)　encoder-decoder　architecture　extracts　higher-level　features　focusing　on　long-range　dependencies.　This　approach　aims　to　combine　the　efficiency　of　MobileNetV2　with　the　feature　extraction　capabilities　of　the　ViT　encoder　for　improved　segmentation　performance.　To　evaluate　DuaSkinSeg＇s　effectiveness,　we　conducted　experiments　on　three　publicly　available　benchmark　datasets:　ISIC　2016,　ISIC　2017,　and　ISIC　2018.　The　results　demonstrate　that　DuaSkinSeg　achieves　competitive　performance　compared　to　existing　methods,　highlighting　the　potential　of　the　dual　encoder　architecture　for　accurate　skin　lesion　segmentation.