# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 4 Experimental Results and Analysis
### Ablation Study  
Ablation experiments were conducted on three datasets to systematically evaluate the contribution of each architectural component. The performance under various configurations is summarized in Table 4. Specifically, when only the Mamba branch was used, the model exhibited a slight decline in most evaluation metrics, indicating that relying solely on structured state-space modeling is insufficient to capture fine-grained spatial features. Upon integrating a convolutional branch to form a dual-branch architecture, segmentation accuracy improved considerably. Further enhancements were observed with the inclusion of the SCDA module. By incorporating both channel and spatial attention mechanisms, SCDA facilitates more effective feature interaction and mitigates spatial information loss. We also examined the effect of adding the SCDA module independently to the Mamba and convolutional branches. Notably, in the dual-branch configuration, the model achieved superior performance while significantly reducing the number of parameters compared to single-branch variants. This finding underscores not only the effectiveness of the hybrid design but also the parameter efficiency afforded by channel-balanced decomposition. Compared to its counterpart without SCDA, the full model yielded higher DSC scores and more precise segmentation boundaries. However, this improvement came at the cost of increased model complexity due to the additional parameters introduced by SCDA. One possible reason for the limited improvements in certain metrics is that the current fusion mechanism between local CNN features and the global representations learned by Mamba has not yet been fully optimized for segmentation tasks. In future work, we aim to further explore and enhance this interaction strategy to fully leverage the advantages of the dual-branch architecture in medical image segmentation. To assess the influence of training data volume on segmentation performance, we conducted experiments using progressively larger subsets of the datasets, corresponding to 25%, 50%, 75%, and 100% of the available samples. The results, presented in Table 5, show a consistent improvement in the DSC metric as the amount of training data increases, highlighting the model's strong scalability and robustness under varying data availability scenarios. Specifically, on the ISIC 2018 dataset, the DSC improved from 87.15% with 25% of the data to 90.32% with the full dataset, reflecting a gain of 3.17 percentage points. A similar trend was observed on Synapse, where the DSC rose from 75.35% to 81.52%. The ACDC dataset exhibited the smallest relative gain, from 88.95% to 92.12%, likely due to its comparatively smaller inter-sample variability. Notably, even with only 25% of the training data, the model maintained competitive performance across all three datasets, demonstrating its strong generalization ability in low-resource settings. These findings underscore HCMUNet's data efficiency and robustness, making it well suited for real-world medical scenarios where labeled data is often limited. Furthermore, the observed performance gains suggest that