## 4 Experimental Results
### Main Result
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Dice Score (\%)**} & \multicolumn{3}{c}{**Hausdorff 95 (mm)**} \\ \cline{2-7}  & **ET** & **TC** & **WT** & **ET** & **TC** & **WT** \\ \hline TransBTS [22] & 78.36 & 81.41 & 88.89 & 5.91 & 7.58 & 7.60 \\ Attention-based [23] & 75.9 & 80.7 & 89.3 & 4.19 & 7.66 & 6.96 \\ Cross-Sequence [24] & 78.09 & 84.32 & 90.81 & 2.88 & 5.74 & 5.27 \\ Two-Stage Cascaded [25] & 80.21 & 86.47 & 90.94 & 3.16 & 5.43 & 4.26 \\
3D-UNet [26] & 70.86 & 72.48 & 87.38 & 5.06 & 8.71 & 9.43 \\ Swin UNETR [27] & 85.2 & 86.9 & 90.8 & 8.78 & 5.62 & 6.23 \\ [PERSON] et al. [28] & 81.33 & 84.08 & 88.62 & 4.21 & 8.02 & 5.46 \\ DualTrans (ours) & 85.63 & 87.2 & 91.42 & 8.32 & 5.64 & 7.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the DualTrans model and other SOTA models based on BraTS 2019 validation set. it to be an excellent example of utilizing the Transformer for global modeling. As a classic model combining a CNN and the Transformer in the field of brain-tumor segmentation, this model has great reference significance for our model transformation. It should be noted that the results of the 3D-UNet model in Table 2 on the BraTS 2019 validation set are based on the comparative experimental analysis results of the TransBTS paper. Compared with the Two-Stage Cascaded model, the brain-tumor regions of ET, TC, and WT were increased by 5.4%, 0.7%, and 0.5% respectively. Compared with the model by [28], the brain-tumor areas such as ET, TC, and WT were increased by 4.3%, 3.1%, and 2.8%, respectively. It can be seen that the improvement effect of our model is relatively obvious. Figure 4 shows the visual segmentation results on the Brats 2019 validation set. The training set of the **BraTS 2021** dataset contains 1251 MRI multimodal brain-tumor cases, which is consistent with the operation of the Brats 2019 dataset. First, a five-fold cross-validation evaluation was performed on the training set. Our dual-path encoder model (DualTrans) was evaluated, and the dice coefficients of WT, ET, and TC were 93.89%, 91.23%, and 89.8%, and the Hausdorff distance 95% (HD) values were 4.36, 5.02, and 3.68, respectively. Table 3 demonstrates the comparison of segmentation accuracy of our proposed DualTrans model with other excellent models on the BraTS 2021 validation set. Figure 4: BraTS 2019 validation set on brain-tumor segmentation visual interface. The effect diagrams of axial, coronal, and sagittal plane segmentation using our proposed DualTrans model are shown, respectively. We uploaded 219 unlabeled MRI multimodal brain-tumor cases from Brats 2021 and evaluated the performance online on the Challenge web. The dice coefficients of the proposed dual-path encoder model DualTrans in the ET, TC, and WT brain-tumor regions on the validation set were 86.23%, 88.12%, and 92.83%, with HD95 values of 6.37, 3.64, and 4.51, respectively. Compared with other excellent models, our proposed DualTrans model has obvious advantages. Compared with the classic Swin UNETR model, the dice coefficients in the ET, TC, and WT brain-tumor regions were increased by 0.43%, \(-\)0.38%, and 0.23%, respectively. The dice coefficients of Swin UNETR in the ET and WT regions were lower than in our proposed model, while in the TC region, it was mainly the segmentation of the label 4 region that was less accurate. The dice coefficient of this model is higher than ours, mainly because our model is more complex, and the overall training dataset of brain-tumor segmentation is small, which fails to reflect the advantages of our model. Secondly, our model does not obtain the feature information of the Swin Transformer encoder path through skip connection during upsampling. Some global feature information is missing. However, adding this path will further increase the computational complexity of the model, but the improvement in the model accuracy is very limited.