# A Medical Image Segmentation Network with Multi-Scale and Dual-Branch Attention
## 4 Experiments and Results
### Ablation Study  
To evaluate the effectiveness of the proposed module in MSDA-Net, we conducted an ablation experiment on the CVC-ClinicDB, 2018 Data Science Bowl, ISIC 2018 and Colon Cancer Slice Dataset. The model primarily consisted of two components: PDA and MRD.  
Figure 8: FLOPs distribution scatter plot of different methods. The horizontal axis represents the compared network model, and the vertical axis represents the parameter value. The blue scatter points represent the FLOPs value of the compared methods, and the red scatter points represent the FLOPS of MSDA-Net.  
Figure 7: Visual comparison of image segmentation results of the Colon Cancer Slice dataset.  
The MRD module mainly obtains dense connections to fuse feature information at different scales to obtain more comprehensive semantic features and achieve clearer boundary segmentation. As demonstrated in Table 5, integrating the MRD into the baseline network resulted in an increase of 0.9% in Dice, 1.7% in IoU, and 0.9% in Recall on CVC-ClinicDB. In the 2018 Data Science Bowl, Dice increased by 1.2%, IoU increased by 0.1%, and Recall increased by 0.2%. On the ISIC 2018, it had an improvement of 0.8% IoU, 1.6% Dice, 1.1% Precision, 0.3% Recall. On the Colon Cancer Slice dataset, Dice increased by 0.5%, IoU increased by 1.3%, and Recall increased by 0.3%. These prove that adding the MRD module can effectively extract multi-scale detail feature information and improve the network segmentation capability. The PDA leverages a parallel dual-branch attention mechanism to focus on the target lesion area and suppress the influence of noise information on the segmentation results. Integrating the PDA module into the baseline network results in a Dice improvement of 1.4%, IoU improvement of 1.3%, and Recall improvement of 1.6% on the CVC-ClinicDB dataset. On the 2018 Data Science Bowl dataset, Dice increased by 0.8%, IoU by 0.3%, and Recall by 0.7%. On the ISIC 2018, it had an improvement of 1.1% IoU, 1.6% Dice, 2% Precision, and 0.5% Recall. On the Colon Cancer Slice dataset, Dice increased by 0.3%, IoU increased by 1%, and Recall increased by 0.6%. These findings confirm that the addition of the PDA module significantly enhanced the network's ability to segment target regions accurately.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Method** & **Acc** & **Dice** & **Precision** & **IoU** & **Recall** \\ \hline \multirow{4}{*}{CVC-ClinicDB} & Baseline & 0.982 & 0.899 & 0.924 & 0.835 & 0.919 \\  & Baseline + MRD & 0.982 & 0.908 & 0.895 & 0.852 & 0.928 \\  & Baseline + PDA & 0.984 & 0.913 & 0.908 & 0.848 & **0.935** \\  & MSDA-Net & **0.992** & **0.917** & **0.911** & **0.870** & 0.934 \\ \hline \multirow{4}{*}{2018 Data Science Bowl} & Baseline & 0.970 & 0.900 & 0.923 & 0.863 & 0.914 \\  & Baseline + MRD & 0.971 & 0.912 & 0.901 & 0.864 & 0.916 \\ \cline{1-1}  & Baseline + PDA & 0.971 & 0.908 & 0.917 & 0.866 & 0.921 \\ \cline{1-1}  & MSDA-Net & **0.979** & **0.918** & **0.925** & **0.871** & **0.938** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiments on different datasets.  
Figure 9: Parameter distribution scatter plot of different methods. The horizontal axis represents the compared network model, and the vertical axis represents the parameter value. The blue scatter points represent the parameters value of the compared methods, and the red scatter points represent the parameters of MSDA-Net.