# Attention-Based Two-Branch Hybrid Fusion Network for Medical Image Segmentation
## 4 Experiments and Analysis of Results
### Experimental Results and Analysis of Different Datasets  
#### 4.3.1 Gastric Cancer Dataset  
This paper uses the BOT gastric slice dataset from 2017 [42]. There are 140 normal portions and 560 sections with stomach cancer in the dataset. Hematoxylin-eosin (H&E) staining was applied to the slices at a 20\(\times\) magnification. The stomach slices have a resolution of 2048 \(\times\) 2048, and the data supplier has partially labeled the tumor location. The size of the deep learning network is too large for direct processing. To create the training set, we clip areas from the stomach picture that measure 224 by 224. The sample patches are shown in Figure 5. Due to the limited data available, all samples underwent horizontal flipping, vertical flipping, and random rotations within [\(-90^{\circ}\), \(90^{\circ}\)] to augment the training set. For experimental analysis, the dataset in this study was partitioned into training and testing subsets at a ratio of 8:2.  
The GLFUnet medical image segmentation approach presented in this study is contrasted with various methods, including U-Net, ResUNet, FCN, DeeplabV3, ConvNeXt, Medical Transformer, TransUnet, SwinUnet, and TransFuse, in order to more thoroughly test the efficacy of this model. Table 1 displays the Dice, mIoU, and Acc statistics for GLFUnet on the dataset for stomach cancer as well as the other nine segmentation models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Dice (\%)**} & \multicolumn{1}{c}{**mIoU (\%)**} & \multicolumn{1}{c}{**Acc (\%)**} \\ \hline \multirow{5}{*}{CNN-based models} & U-Net & 83.67 & 72.43 & 88.63 \\  & Res-UNet & 86.81 & 72.08 & 88.92 \\  & FCN & 84.92 & 69.72 & 86.95 \\  & DeepLabV3 & 87.67 & 77.44 & 88.10 \\  & ConvNeXt & 88.05 & 73.75 & 88.50 \\ \hline \multirow{5}{*}{Transformer-based models} & MedT & 87.12 & 72.86 & 88.06 \\  & TransUnet & 90.76 & 79.07 & 91.46 \\ \cline{1-1}  & SwinUnet & 91.21 & 79.84 & 92.25 \\ \cline{1-1}  & TransFuse & 90.28 & 79.72 & 92.16 \\ \hline \multicolumn{1}{c}{Ours} & GLFUnet & 91.65 & 79.87 & 92.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data for each indicator on the gastric cancer dataset.  
Figure 5: Diagram of the pathologic samples from the stomach. (**a**,**b**) are Tumor, (**c**) is Normal.  
ConvNeXt performs better than other CNN-based models, as evidenced by its testing results, which show 88.05%, 73.75%, and 88.50% on Dice, mIoU, and Acc, respectively. Among these Transformer-based models, SwinUnet performs the best, reaching 91.21%, 79.84%, and 92.25% for Dice, mIoU, and Acc, respectively; our proposed GLFUnet performs better than ConvNeXt and SwinUnet, two current state-of-the-art methods, with 91.65%, 79.87%, and 92.51%, respectively. As a result, GLFUnet performs the best on the dataset for stomach cancer. The improvement rate is 0.44%, 0.03%, and 0.26%, respectively, when compared to the best Transformer-based model SwinUnet; the improvement rate is 3.60%, 6.12%, and 4.01%, respectively, when compared to the best CNN-based model ConvNeXt.  
To provide a clearer comparison, Figure 6 below presents a visual contrast of selected segmentation outcomes for gastric cancer pathology images. It is evident that the Transformer-based architecture emphasizes global contextual information extraction and excels in remote relationship modeling, yielding sharper edges compared to the results from the CNN-based model. GLFUne indicates that the segmentation outputs resulting from the integration of Swin Transformer and ConvNeXt via the AtFF module are more aligned with the annotated mask images, further substantiating the efficacy of our suggested approach. The GLFUnet segmentation exhibits superior performance.