Residual Mamba-Driven Multiscale Attentive Network With Boundary Enhancement for IoT-Enabled Medical Image Segmentation

In IoT-enabled intelligent healthcare systems, medical images are frequently acquired in real-time from heterogeneous imaging sensors such as dermoscopic devices and MRI scanners. In resource-limited or edge-deployed settings, achieving precise and rapid image segmentation plays a crucial role in facilitating early diagnosis and supporting clinical decision-making. This paper proposes a medical image segmentation method based on a residual Mamba backbone network, combining a multi-scale gated attention (MGA) module with a boundary enhancement (BE) module to effectively enhance the model’s feature representation and boundary localization capabilities. Specifically, the R-Mamba backbone network combines the advantages of statespace modeling and convolutional feature extraction, achieving efficient fusion of global context and local details. The MGA module dynamically captures multi-scale semantic information through dilated convolutions and gating mechanisms, enhancing the model’s adaptability to targets of different scales and shapes. The BE module significantly strengthens boundary representation and fine-grained structural segmentation through multi-scale convolutions and channel-spatial dual attention mechanisms. Additionally, this paper designs a multi-loss function joint optimization strategy to comprehensively constrain region overlap, pixel classification, and structural consistency. Experimental validation on ISIC skin lesion and LGG brain tumor datasets shows competitive performance compared to several mainstream models under the tested conditions.