# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 2 Related Work  
### U-Shaped Model for Medical Image Segmentation  
CNNs constitute a fundamental component of deep learning and were originally developed for visual classification tasks. The advancement of artificial intelligence has significantly accelerated the adoption of models like Fully Convolutional Networks (FCNs) [16] in image segmentation tasks. In contrast to natural images, medical images typically possess higher complexity and require more accurate feature extraction. U-Net, the first CNN-based model tailored for medical image segmentation, incorporates skip connections to merge detailed spatial and abstract semantic features, significantly improving segmentation results. In the ISBI 2015 Cell Tracking Challenge, U-Net improved the Intersection over Union metric by approximately 10% compared to traditional image processing methods and ranked first in multiple sub-tasks, demonstrating its strong modeling capacity and superior accuracy. By embedding dense skip connections within a nested architecture, UNet++ [17] extends U-Net to facilitate feature reuse and enhance segmentation performance. Experiments indicate a 3% to 5% increase in Dice scores over U-Net across multiple benchmark datasets. ResUNet++ [18] builds upon UNet++ by integrating residual blocks and dilated convolutions, thereby improving global feature perception and delivering an approximate 5% increase in segmentation accuracy on the skin cancer dataset. Attention U-Net [19] further improves U-Net by incorporating attention gates that selectively focus on relevant feature regions. This design yields a 1% to 3% improvement in Dice scores and reduces boundary errors, leading to better precision and robustness in medical image segmentation. UNetV2 [20] presents an improved skip connection design that leverages Hadamard product-based fusion to better combine deep feature representations with detailed spatial cues. Its segmentation accuracy surpasses that of existing state-of-the-art models on skin lesion and polyp benchmarks. While CNNs have achieved notable success, the limited spatial extent of their receptive fields restricts their ability to capture global context and learn complex feature dependencies. This constraint can impair the richness of feature representations and negatively impact segmentation accuracy. To overcome these issues, recent research has increasingly explored SSMs as a promising alternative to conventional attention-based architectures. Mamba introduces a selective State Space architecture that achieves strong representational capacity by dynamically controlling input selection through linear recurrent operations. Unlike Transformers, Mamba avoids the quadratic complexity of self-attention, improving scalability for high-resolution medical images. Its efficient modeling of long-range dependencies makes it a compelling approach for advancing semantic segmentation in this domain. Recent advancements in Mamba-based architectures have resulted in the creation of models such as U-Mamba, SegMamba [21], and their variants, which have been successfully incorporated into encoder-decoder frameworks for medical image segmentation. For example, U-Mamba enhances nnUNet [22] with the Mamba architecture, leading to a roughly 2% improvement in segmentation accuracy and outperforming ViT-based models by a significant margin. SegMamba reduces both computational cost and memory usage relative to SwinUNETR [23], while also exhibiting more stable training behavior. Leveraging these advantages, it achieves an increase of around 7% in Dice score. Swin-UMamba [24] extends U-Mamba by integrating ImageNet-pretrained weights, achieving approximately a 50% reduction in parameters while maintaining comparable segmentation performance. SliceMamba [25] introduces a bidirectional slice scanning module to enhance local feature modeling, particularly excelling in boundary-sensitive segmentation tasks. It achieves a 25% reduction in the HD95 metric compared to Swin-UNet [26].