YESnet: YOLOv11 Enabled SAM-2 Framework for Memory-Efficient Skin Lesion Segmentation

This paper presents YESnet, a novel framework integrating the YOLOv11 object detector with the Segment Anything Model 2 (SAM-2) for memory-efficient skin lesion segmentation. The proposed architecture introduces: (1) An anchor-free YOLOv11 detection head that eliminates predefined bounding box priors while maintaining detection accuracy, (2) A memory-optimized SAM-2 variant that preserves boundary delineation capabilities through compressed feature retention, and (3) A lightweight co-processing pipeline that coordinates detection and segmentation stages without computational redundancy. Evaluated on the HAM10000 dataset, YESnet achieves competitive performance in lesion segmentation (96.00% accuracy, 92.38% Dice) while demonstrating efficient memory utilization. The framework's design enables effective lesion boundary capture with minimal parameter overhead, making it particularly suitable for resource-constrained clinical deployment scenarios.