Dermoscopic image segmentation faces significant challenges due to complex lesion morphology, blurred boundaries and noise interference. This study proposes a deep learning model named MelanoFusionNet, which employs a dual-branch encoder composed of ResNet50 and an improved Mamba VSS module. A multi-scale attention fusion decoder (MSAFD) is introduced to integrate both local and global information. Within the decoder, the extended kernel grouped gate (EKGG) and the hierarchical scale-aware attention module (HSAM) enhance feature fusion and boundary precision, while the depthwise upsample refinement block (DURB) improves reconstruction efficiency. The model is trained with a weighted combination of Dice and boundary losses. Experiments on the ISIC2018 dataset demonstrate that MelanoFusionNet outperforms mainstream methods across multiple metrics and provides reliable support for computer-aided skin cancer diagnosis.