## 4 Experimental Results and Discussion
### Experimental Results
#### 4.4.2 Comparison with Other U-Net Architectures  
We evaluate our proposed method with state-of-the-art U-Net architectures in semantic segmentation tasks, such as Attention U-Net [17; 18] and U-Net++ [15; 16]. For experiments on the CVC-ClinicDB dataset, the baseline U-Net yields a dice coefficient of 65.93%. In Table 4, Attention U-Net and U-Net++ achieve accuracy of 67.57% and 68.89%, respectively. Our proposed scAG boosts performance to 73.31%, which increases an approximately seven-point gap when compared to the standard U-Net model though adding small parameters, as shown in Table 4. Besides, for the lung \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Dice Score (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline U-Net + spatial + channel (ratio = 16) & \(\mathbf{71.72\pm 6.81}\) & \(69.81\pm 8.97\) & \(\mathbf{74.21\pm 4.56}\) \\ U-Net + spatial + channel (ratio = 8) & \(70.55\pm 4.98\) & \(\mathbf{70.44\pm 6.29}\) & \(73.04\pm 5.52\) \\ U-Net + spatial + channel (ratio = 2) & \(70.76\pm 5.98\) & \(70.05\pm 9.76\) & \(73.97\pm 3.80\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different channel methods on CVC-ClinicDB dataset (best results marked bold). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dice Score (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline U-Net + spatial (avg-max) + channel & \(70.18\pm 4.63\) & \(\mathbf{71.52\pm 8.66}\) & \(72.04\pm 3.13\) \\ U-Net + spatial (1 \(\times\) 1 conv) + channel & \(70.15\pm 5.49\) & \(67.62\pm 8.21\) & \(73.94\pm 4.84\) \\ U-Net + spatial (avg-max-conv) + channel & \(\mathbf{71.72\pm 6.81}\) & \(69.81\pm 8.97\) & \(\mathbf{74.21\pm 4.56}\) \\ \hline U-Net + spatial (k = 7) + channel & \(\mathbf{71.72\pm 6.81}\) & \(69.81\pm 8.97\) & \(\mathbf{74.21\pm 4.56}\) \\ U-Net + spatial (k = 5) + channel & \(70.47\pm 5.12\) & \(70.05\pm 7.32\) & \(73.35\pm 5.14\) \\ U-Net + spatial (k = 3) + channel & \(70.94\pm 6.20\) & \(\mathbf{71.06\pm 10.00}\) & \(73.31\pm 3.94\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different spatial methods on CVC-ClinicDB dataset (best results marked bold). tumor dataset provided by VIP-CUP, our proposed scAG shows a dice coefficient of 56.26% and also outperforms the Attention U-Net and U-Net++ with a performance of 53.15% and 54.66%, respectively. For the brain dataset from TCGA, our proposed scAG still surpasses Attention U-Net and U-Net++ with a dice score of 85.83%. Attention U-Net was designed using the attention gate model, which is beneficial for locating the target object, but lacks focus on different semantic information between the encoder and decoder features. U-Net++ was proposed by redesigning the dense skip connection to alleviate the gap semantic between the encoder and decoder features, and it thus offers better segmentation results. By capturing useful information from both spatial and channel information using scAG, our proposed U-Net outperforms other state-of-the-art U-Net architectures. In Table 5, the inference time is presented between other U-Net architectures and our proposed. These models were trained and tested on Intel i7-4790 CPU and Nvidia GTX 1070 GPU. The GPU handled procedures related to CNN. The latency of our proposal takes more than 2.3 times, 1.5 times, and 1.2 times compared to U-net, Attention U-net and U-net++ architecture, respectively. Although the scAG takes more time to extract rich location information of objects and semantic information, it is not considerable to be utilized for real-time segmentation systems. There is a trade-off between speed and accuracy, better speed is lower accuracy, and vice versa. Representative results are also depicted in Figure 6. For example, in polyp segmentation, U-Net manages to segment the polyp with a low-level performance with a poor shape of the segmentation. By focusing on salient regions, the shape result of the Attention U-Net looks better, but it still is not good enough.