## 4 Experiment and Analysis
### Results and Discussion  
To validate the effectiveness of our network, we conducted a comparison with current open-source and widely used medical image segmentation networks, including U-Net, UNet++, SegNet, Attention U-Net, UNeXt, and ResU-Net. All models were deployed and executed locally, with all experimental variables being consistent except for the network architecture. Figure 6 showcases the outcomes of breast tumor segmentation alongside actual manual annotations. When assessing the segmentation results as a whole, our model demonstrates the capability to accurately locate lesion regions and determine their shape and size. In terms of segmentation intricacies, our model outperforms manual annotations, delivering more detailed and refined results. Notably, even in the case of fuzzy and unclear tumor regions, as highlighted in the red box, our model still achieves precise segmentation. The experimental data further validate the efficacy of our model. Results from various models tested on both Dataset B and the BUSI dataset are summarized in Tables 2 and 3. Notably, on the Dataset B dataset, our model exhibits a 1.72% improvement in IoU and a 0.84% improvement in the Dice score. On the BUSI dataset, the improvements are even more pronounced, with a 2.25% increase in IoU and a significant 2.58% improvement in the Dice score. While our model may not have achieved the highest scores in individual metrics, overall, it has achieved a notably high level of segmentation quality. Importantly, our model excels in the two most critical segmentation indicators, Dice and IoU, which underscores its effectiveness in accurately delineating breast tumor lesions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dice** & **IoU** & **Recall** & **Precision** & **Accuracy** \\ \hline U-Net [24] & 86.28 & 75.89 & **87.15** & 85.75 & 99.15 \\ U-Net++ [25] & 83.84 & 72.20 & 85.17 & 82.79 & 98.99 \\ SegNet [23] & 83.51 & 71.86 & 80.33 & 87.49 & 99.05 \\ Attention U-Net [26] & 84.27 & 72.86 & 84.46 & 84.70 & 99.05 \\ UNeXt [30] & 81.09 & 68.57 & 80.09 & 81.66 & 98.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results with other models on Dataset B.  
Figure 6: The segmentation results of ours and ground truth. feature extraction module. Additionally, the skip connection operation fails to consider both spatial and channel dimensions simultaneously, and pooling operations result in the loss of valuable feature information. To tackle these challenges, we introduced a novel breast tumor segmentation network known as RMAU-Net, which combines the power of residual depthwise separable convolution and multi-scale channel attention gates. We have designed the RDw block, which boasts a straightforward structure capable of capturing more comprehensive global characteristics of breast tumors. Simultaneously, the MCAG block is devised to rectify low-level features across both spatial and channel dimensions, aiding in the effective learning of non-regular breast tumor features and facilitating high-level feature recovery during up-sampling. Furthermore, our approach replaces traditional pooling methods with Patch Merging operations to prevent the loss of critical breast ultrasound image information. Our experimental results on two distinct datasets demonstrate that RMAU-Net outperforms existing methods in terms of segmentation accuracy. In the future, we plan to further enhance RMAU-Net to handle more challenging breast lesion images and explore lightweight model designs to achieve more efficient breast tumor segmentation. Conceptualization: S.Y. ; methodology: S.Y. ; formal analysis and investigation: S.Y. ; writing--original draft preparation: S.Y., Z.Q., and P.L. ; writing--review and editing: Z.Q., P.L., and Y.H. ; funding acquisition: Z.Q. ; resources: Z.Q. ; supervision: Z.Q. All authors contributed to the article and approved the submitted version. All authors have read and agreed to the published version of the manuscript.