## 4 Experiments and Results
### Ablation Experiments  
#### 4.5.1 Ablation Study of Each Module in SCENet  
We conducted ablation experiments to verify the effect of different modules in this architecture. Experiment (Expt) A represents the baseline without any modules. The SCER module, CSAM attention mechanism, and ASPP module are used in Experiments B, C, and D, respectively. The different combinations of SCER module, CSAM attention mechanism, and ASPP module are tested in Experiments E, F, and G, respectively.  
Figure 7: Visualization results of medical cases. The union of green, yellow and red, the union of red and yellow, and the yellow labels represent WT, TC, and ET, respectively.  
The WT value of the combination with SCER and CSAM is the highest, which can be seen in Figure 8 and Table 3. From the results, it can be seen that the effect is not significant when using CSAM or ASPP alone, but there is a significant improvement when both are used in the network. This indicates that increasing the multi-scale receptive field in the bottleneck layer is beneficial, but without effective feature selection after input to the decoder, it is difficult to achieve an improved performance. Empirically, we used the SCER, CSAM, and ASPP; the average dice is the best, and the average dice result is 87.57%. The best results show that our network and all the modules can be effectively applied to brain tumor segmentation tasks.  
4.5.2 Ablation Study of the Number of Stacking Convolution Layers and Kernel Size in the SCER Module  
To verify the effectiveness of replacing a convolution of kernel size 7 \(\times\) 7 \(\times\) 7 with a stack of three convolutions layers of kernel size 3 \(\times\) 3 \(\times\) 3 in the SCER module, we conducted three sets of experiments in the SCER.  
The results are shown in Table 4 and Figure 9. It is generally believed that a convolution with kernel size 7 \(\times\) 7 \(\times\) 7 has a large receptive field and that its effect should be the best, but, except for the best ET results, its other indications are not as good as those of other experiments. A large receptive field does indeed improve the segmentation of small target areas, but a large number of parameters can lead to a decrease in the final result. Three convolutions with kernel size 3 \(\times\) 3 \(\times\) 3 and a convolution with kernel size 7 \(\times\) 7 \(\times\) 7 have the same receptive field, but the parameter of three 3 \(\times\) 3 \(\times\) 3 is much smaller, whose results are the best among these three experiments. Empirically, we reduced one convolution on the basis of Experiment C. From Experiment B in Table 4, it can be seen that the effect did  
Figure 8: The result of ablation study of each module in SCENet. The symbol “\(\surd\)” indicates that it has been selected for use in the network.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Expt**} & \multirow{2}{*}{**SCER**} & \multirow{2}{*}{**CSAM**} & \multirow{2}{*}{**ASPP**} & \multicolumn{6}{c}{**Dice(\%)**} \\ \cline{5-8}  & & & & **WT** & **TC** & **ET** & **AVG** \\ \hline
**A** & & & & & 90.06 & 82.68 & 80.88 & 84.54 \\
**B** & \(\surd\) & & & & 91.15 & 86.40 & 82.17 & 86.57 \\
**C** & & & \(\surd\) & & 91.40 & 85.51 & 81.99 & 86.30 \\
**D** & & & & \(\surd\) & 90.74 & 85.79 & 82.96 & 86.50 \\
**E** & \(\surd\) & & \(\surd\) & & **91.75** & 86.82 & 81.99 & 86.85 \\
**F** & \(\surd\) & & & \(\surd\) & 91.11 & 86.52 & 82.31 & 86.65 \\
**G** **(SCENet)** & \(\surd\) & \(\surd\) & \(\surd\) & 91.67 & **87.70** & **83.35** & **87.57** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The result of ablation study of each module in SCENet. The symbol “\(\surd\)” indicates that it has been selected for use in the network.  
not improve, which also shows that stacking three 3 \(\times\) 3 \(\times\) 3 convolutional layers is the best method in the proposed network.