YMamba: A Dual-Branch Network Fusing State Space Model and CNN for Medical Image Segmentation

Intelligent technologies like deep learning have significantly improved medical image segmentation, enhancing clinical decision-making and reducing healthcare costs. However, CNN-based methods face limitations due to constrained receptive fields and semantic information loss in deeper layers. Consequently, to address these issues, we innovatively propose the YMamba model which employs a parallel hybrid architecture of CNN and VMamba, enhancing the modeling capability of distant features while maintaining local feature detail textures in medical images without introducing additional parameters. Additionally, our proposed DBFM module employs an enhanced attention strategy to integrate the strengths of both methods more effectively, reinforcing image feature representation and mitigating background noise. Finally, the MCFFD module receives shallow and deep features from the fusion module, addressing the challenge of size variation in target segmentation regions within medical images. Extensive experiments demonstrate that YMamba achieves state-of-the-art results on four medical image datasets BUSI, DDTI, TN3K, and ISIC2016.