# An Enhanced Feature Extraction Network for Medical Image Segmentation
## 4 Experiment
### Datasets and Results
#### 4.2.2 Results on CVC-ClinicDB Dataset  
The segmentation of polyp images is a challenging task, which is mainly because the demarcation is indistinct between the polyp and its surrounding mucosa and the different sizes, colors and textures of the polyps with the same type. CVC-ClinicDB is an open access colonoscopy image database which is used in our experiments. The CVC-ClinicDB dataset contains 612 images with a resolution of 384 \(\times\) 288 from 31 colonoscopy sequences. We first split the dataset into training, validation, and testing sets at a ratio of 8:1:1. Then, we used data augmentation methods to obtain 12,714 training images and 1586 validation images. The experimental results of each method in this dataset are shown in Table 2.  
Table 2 shows the results on the CVC-ClinicDB testing dataset. Compared with U-Net and the recent works, we observe that the EFEN improves performance remarkably. The comparison methods in this table can be summarized into two categories: one is those based on CNN, and the other is those based on transformer. The methods based on CNN are almost similar to U-Net when using skip-connection; that is, they all add the features extracted by the encoder to the output of the corresponding layer of the decoder indiscriminately. It will inevitably introduce noise information into the decoder, thus interfering with the subsequent segmentation results. However, there are still gaps compared with the state-of-the-art methods. For example, SSFormer-L achieves a DSC of 94.47%. This is because SSFormer-L is a method based on transformer which lacks inductive bias in CNN, and it is based on two datasets. This kind of method needs a lot \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **DSC (\%)** & **mIoU (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline TransUNet [48] & 86.76 & 79.91 & 87.34 & 87.63 \\ LeViT-UNet [49] & 82.82 & 75.48 & 82.68 & 84.99 \\ Multi-scale patch-based CNN [50] & 81.30 & - & 78.60 & 80.90 \\ ResUNet++ [51] & 85.40 & 78.11 & 85.39 & 87.05 \\ Conditional generative adversarial network [52] & 88.48 & 81.27 & - & - \\ U-Net [7] & 87.81 & 78.81 & 78.65 & 93.29 \\ DoubleU-Net [12] & 92.39 & 86.11 & 84.57 & 95.92 \\ PraNet [53] & 89.60 & 84.90 & - & - \\ ResUNet++ + CRF [54] & 92.03 & 88.98 & 93.93 & 84.59 \\ TransFuse-S [55] & 91.8 & 86.8 & - & - \\ AG-CUResNeSt [56] & 91.70 & 86.7 & - & - \\ UACANet-S [57] & 91.6 & 87.00 & - & - \\ SSFormer-L [58] & 94.47 & 89.95 & - & - \\ EFEN & 93.04 & 87.25 & 86.99 & 95.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons on CVC-ClinicDB testing set. of pre-training data to obtain better results, or it will be worse. For example, the results of TransUNet and LeViT-UNet are even lower than U-Net. For the smaller dataset, CNN is still the prior choice. However, compared with the baseline U-Net, EFEN outperforms U-Net by a large margin, with 5.23% improvement on DSC and 8.44% improvement on mIoU. In addition, compared with recent works, EFEN has a DSC of 93.04%, which is superior to DoubleU-Net (92.39%) by 0.65%. A careful visual analysis of the result is shown in Figure 4. In the first two columns of Figure 4, we can see that U-Net misclassified the parts that do not belong to the target. It can be seen from columns 3-6 in Figure 4 that because the boundary between polyps and their surrounding mucosa is unclear, and polyps have different sizes, colors, and textures, it is difficult for U-Net to segment the target accurately. However, our network can segment some smaller targets and targets with unclear edges more accurately. See the red boxes in Figure 4 for more details. Although our improved skip-connection can make up for some missing information, compared with the mask, our method still has some inaccuracies, such as the second, fourth and last columns. There are two main reasons for this issue. First, the quality of samples includes the quality of samples collected and the quality marked by professional doctors should be enhanced.