## 4 Experiment
### Comparison with Other Methods  
The performance of the proposed MSR-Net was evaluated on an intracranial hemorrhage dataset and compared to other methods, including U-Net [25], Attention U-Net [28], \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Module** & **Dice** & **Jaccard** & **Sensitivity** & **Precision** \\ \hline U-Net(baseline) & 0.610 & 0.491 & 0.644 & 0.632 \\ U-Net + ip & 0.625 & 0.503 & 0.674 & 0.620 \\ U-Net + ip + ml & 0.661 & 0.539 & 0.703 & 0.684 \\ U-Net + ip + ml + CA & 0.669 & 0.549 & 0.720 & 0.608 \\ U-Net + ip + ml + CA + MRHDC & 0.687 & 0.567 & 0.773 & 0.642 \\ U-Net + ip + ml + CA + ms & 0.673 & 0.554 & 0.685 & 0.702 \\
**U-Net + ip + ml + CA + ms + MRHDC** & **0.717** & **0.596** & **0.779** & **0.712** \\ \hline \hline \end{tabular} Note: ip: Image preprocessing: ml: Multi-objective loss function; ms: MSpool module.  
\end{table} 
Table 1: Performance comparison of different segmentation module. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MRHDC** & **Dice** & **Jaccard** & **Sensitivity** & **Precision** \\ \hline Group 1 & 0.660 & 0.533 & 0.682 & 0.690 \\ Group 2 & 0.687 & 0.565 & 0.697 & 0.675 \\
**Group 3** & **0.717** & **0.596** & **0.779** & **0.712** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Segmentation results comparison of different groups of HDC structures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MRHDC** & **Dice** & **Jaccard** & **Sensitivity** & **Precision** \\ \hline R = [1,2,3] & 0.684 & 0.560 & 0.770 & 0.638 \\ R = [1,2,4] & 0.717 & 0.596 & 0.779 & 0.712 \\
**R = [1,3,4]** & **0.677** & **0.553** & **0.778** & **0.634** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Segmentation results comparison of MRHDC modules with different expansion rates. and CMUNet [41]. Except for U-Net, the other three networks contain different attention mechanisms. By adding the same attention mechanism, the effectiveness of the designed network module can be discerned through the Dice index and Jaccard index. All networks share identical training strategies, including learning rate, epoch, optimal model storage method, and optimizer. While training U-Net, the traditional cross-entropy loss function was used as the baseline. To emphasize the effectiveness of the proposed module on small focus segmentation, a multi-objective joint loss function was chosen as the training loss function for other networks. Figure 11 provides a visual representation of the segmented hemorrhage lesions, with black areas signifying the background and white areas indicating the lesions. The effectiveness of the MSRL-Net can be observed intuitively in Figure 11. Although other networks can predict approximate bleeding locations when the bleeding area is large and distinct, they may produce inaccurate predictions. However, the proposed network delivers more precise positioning and shape. Moreover, in areas with severe disease, including small lesion areas and images with less discernible features, our network can still predict bleeding areas, while other networks may fail to recognize relevant features, leading to all-black prediction maps or false negative errors in spatial isolation. In the experiment, the developed model outperformed prior techniques, yielding satisfactory segmentation outcomes. Under identical conditions and datasets, the performance of the proposed model was compared to architectures such as U-Net. Table 5 presents the comparative analysis, clearly indicating the superiority of the proposed method over U-Net and Attention U-Net. As evidenced by Table 5, the Dice coefficient, Jaccard index, sensitivity, and precision of our network reached 0.717, 0.596, 0.779, and 0.712, respectively. In comparison to the U-Net network, the proposed model's Dice coefficient and Jaccard index increased by 0.107 and 0.105, respectively. Based on prior literature, our segmentation performance is unsurpassed.