# Attention-Based Two-Branch Hybrid Fusion Network for Medical Image Segmentation
## 4 Experiments and Analysis of Results
### Experimental Results and Analysis of Different Datasets
#### 4.3.2 Liver Cancer Dataset  
Images of liver cancer from five patients were obtained from the Third Xiangya Hospital of Central South University in China [43]. Qualified pathologists carefully marked the tumor and healthy tissue regions in each sample, with an approximate resolution of about 40,000-60,000 \(\times\) 30,000-50,000. To ensure a smoother overall segmentation outcome, overlapping was allowed between adjacent patches, set at half the patch size both horizontally and vertically [43; 44]. The selection of the sliding window size also considered the number of samples and the resolution of the pathological images. Employing a sliding  
Figure 6: Visualization of the segmentation results of the gastric cancer dataset.  
window with dimensions of 448 \(\times\) 448 and a stride of 224, the liver samples were sectioned into small patches. Subsequently, based on the model size, the stride was adjusted to 224 \(\times\) 224. Figure 7 illustrates a representative liver pathology sample diagram. For experimental purposes, the dataset used in this study was divided into training and test sets at an 8:2 ratio.  
The GLFUnet medical image segmentation approach presented in this study is contrasted with various methods, including U-Net, ResUNet, FCN, ConvNeXt, Medical Transformer, TransUnet, SwinUnet, TransFuse and DHUnet, in order to more thoroughly test the efficacy of this model. Table 2 presents the Dice, mIoU, and Acc scores for GLFUnet on the liver cancer dataset, alongside those of nine other segmentation models.  
ConvNeXt performs better than other CNN-based models, as evidenced by its experimental results, which show 92.69%, 86.52%, and 97.24% on Dice, mIoU, and Acc, respectively. Our proposed GLFUnet model has metrics figures of 93.36%, 86.93% and 97.51% for Dice, mIoU and Acc, respectively. Among the Transformer-based models considered, DHUnet achieves top scores of 92.76%, 86.64%, and 97.43%. Compared to the leading Transformer-based model DHUnet, GLFUnet sees improvements of 0.6%, 0.29%, and 0.08% respectively, while it enhances by 0.67%, 0.41%, and 0.27% over the best CNN-based model ConvNeXt. Consequently, GLFUnet yields the highest performance on the liver cancer dataset. In order to compare more intuitively, Figure 8 below displays a visual comparison of the segmentation results for liver cancer pathology images, showing that the GLFUnet model is closer to the annotated mask images.  
\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{4}{*}{CNN-based models} & U-Net & 92.36 & 86.08 & 97.05 \\  & Res-UNet & 91.59 & 84.73 & 96.67 \\  & FCN & 91.81 & 85.23 & 96.74 \\  & ConvNeXt & 92.69 & 86.52 & 97.24 \\ \hline \multirow{4}{*}{Transformer-based models} & MedT & 90.87 & 83.56 & 96.76 \\  & TransUnet & 91.53 & 84.67 & 96.66 \\ \cline{1-1}  & SwinUnet & 91.70 & 84.53 & 97.12 \\ \cline{1-1}  & TransFuse & 90.68 & 82.93 & 96.97 \\ \cline{1-1}  & DHUnet & 92.76 & 86.64 & 97.43 \\ \hline Ours & GLFUnet & 93.36 & 86.93 & 97.51 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data for each indicator on the livercancer dataset.  
Figure 7: Diagram of the pathologic samples of the liver. (**a,b**) are Tumor, (**c**) is Normal.