Segmentation of lesion areas in dermoscopic images is key to diagnosing melanoma. However, it remains challenging due to the blurring boundary between the lesion area and the normal skin and the presence of interfering factors such as hair occlusion. To address the above challenges, a multi-scale feature interaction fusion network (MSFINet) is proposed. First, to unify the semantic features at different stages and capture more diverse feature information, a Channel Interaction Transformer (CIT) module and a Channel Refinement Attention (CRA) module are proposed. Then, to improve the utilization of features and help the model to resist interference, the Multi-Kernel Dual Branch Aggregation (MKDA) module is designed. This module extracts features from different perspectives to enhance the model’s capability of grasping global contextual information. In the decoder part, a Boundary Difference Optimization (BDO) module is proposed, which aggregates the boundary difference information between high-level features and low-level features to assist the decoder’s feature refinement and make the network more sensitive to boundary information. The proposed method is compared with other methods on three datasets: ISIC 2017, ISIC 2018, and PH2. The DSC scores increase by 1.71%, 1.36%, and 0.87%, respectively, compared to the suboptimal network. The code is available at: https://anonymous.4open.science/r/MSFINet-6E52.

Accurate image segmentation of skin lesions is crucial for the detection and treatment of skin cancer. Based on the modern state space model Mamba, a novel hybrid CNN-Mamba network (BEFNet) is proposed. Specifically, BEFNet introduces Visual State Space (VSS) blocks as the main encoder for extracting and aggregating global information. To improve the expressiveness and utilization of the features, the Adjacent Feature Interaction Module (AFIM) is proposed, and the information lost during the channel interaction process is compensated by the Knowledge Retrospective Module (KRM). To compensate for Mamba’s lack of attention to local detail information, an auxiliary encoder is constructed based on CNN blocks, and a Boundary Optimization Auxiliary Module (BOA) is designed to obtain richer boundary detail information. Finally, a Refined Feature Guidance Module (RFGM) was designed to use the enhanced boundary information as a priori information to guide feature fusion. We conducted experiments on three publicly available skin cancer datasets, and the experimental results demonstrate that BEFNet has competitive performance on the task of skin lesion image segmentation.