# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 1 Introduction  
With advancements in computing technology and hardware, computer-aided diagnosis has become increasingly prominent in modern medicine. Among its applications, medical image segmentation stands out as particularly critical and remains one of the most challenging tasks in image analysis. Accurate segmentation enables precise delineation of anatomical structures and quantitative assessment of organ and lesion morphology, thereby supporting disease classification, progression monitoring, and personalized treatment planning. These capabilities significantly enhance diagnostic accuracy and drive advances in medical technology. Medical image segmentation is predominantly addressed using deep neural networks, particularly Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) [1], both of which have proven to perform exceptionally well on a variety of benchmarks. CNN-based architectures have garnered significant attention for their strong feature extraction capabilities, especially following the introduction of U-Net [2]. However, convolutional operations are inherently limited, with constraints such as local feature focus, poor modeling of long-range dependencies [3], and fixed receptivefield sizes [4]. In contrast, ViT-based segmentation models leverage global self-attention mechanisms to effectively capture long-range dependencies, thereby enhancing segmentation accuracy. Nevertheless, this advantage comes at the cost of increased computational complexity, owing to the quadratic nature of self-attention. To mitigate this, the Pyramid Vision Transformer [5] introduces variable-sized attention windows across multiple resolution levels. This approach not only boosts recognition accuracy by 8.7% but also reduces the parameter count by nearly 50%. Similarly, the Swin Transformer [6] employs a shifted window mechanism to constrain the attention scope, achieving a 70% reduction in computational cost while surpassing standard ViTs in segmentation accuracy. Although the above methods effectively reduce model parameters and computational complexity, practical constraints such as limited computational power and memory resources must still be considered in clinical and real-world healthcare settings. Low parameter counts and minimal memory usage are fundamental requirements for mobile medical applications. Therefore, designing segmentation algorithms that deliver high accuracy with minimal computational demand is essential for deployment on portable and low-power medical devices. Recently, State Space Models (SSMs) [7] have attracted growing interest due to their efficiency and suitability for lightweight model design. Additionally, their strength in modeling long-range interactions contributes significantly to the integration of global semantic context. In contrast to traditional State Space Models, the Mamba model [8] enhances dynamic modeling by incorporating time-varying parameters, which allows it to adapt more flexibly to complex data. When processing text data, Mamba requires significantly fewer parameters than standard Transformers, leading to improved computational efficiency and scalability. Additionally, Mamba introduces a selective scanning mechanism, which enables it to process large-scale data approximately five times faster than Transformer models. Vision Mamba [9] significantly extends the model's applicability to computer vision tasks. While maintaining modeling capabilities comparable to those of Vision Transformers, Vision Mamba demonstrates nearly threefold speed improvements over ViT-based models while reducing GPU memory consumption by about 86% in the context of batch inference for image feature processing. VMamba [10] presents a 2D selective scanning module, effectively bridging one-dimensional sequence modeling and two-dimensional spatial feature extraction. This innovation leads to a 3% improvement in performance on the ImageNet-1K dataset. U-Mamba [11] is the first architecture to integrate Mamba with CNNs for medical image segmentation.