BEFNet: A Hybrid CNN-Mamba Architecture for Accurate Skin Lesion Image Segmentation

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.