## 4 Experimental Results and Discussion
### Experimental Results
#### 4.4.2 Comparison with Other U-Net Architectures
By using the dense skip connection at different scales, U-Net++ has created rough boundaries of the segmentation. Our proposed method generates superior result with a reasonable shape similar to the ground truth. With a small tumor of the lung dataset, U-Net fails to predict the fine shape of the tumor. Although Attention U-Net and U-Net++ have a competitive \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Deep Supervision** & **Dataset** & **Dice Score (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline \multirow{3}{*}{U-Net [11]} & \multirow{3}{*}{\(\times\)} & CVC-ClinicDB & \(65.93\pm 6.34\) & \(61.49\pm 7.30\) & \(70.00\pm 6.62\) \\  & & VIP-CUP18 & \(52.47\pm 3.83\) & \(48.14\pm 6.45\) & \(54.44\pm 3.40\) \\  & & TCGA & \(83.52\pm 1.61\) & \(77.79\pm 2.90\) & \(83.44\pm 2.30\) \\ \hline \multirow{3}{*}{Attention U-Net [17; 18]} & \multirow{3}{*}{\(\times\)} & CVC-ClinicDB & \(67.57\pm 6.47\) & \(64.03\pm 7.13\) & \(71.68\pm 5.96\) \\  & & VIP-CUP18 & \(53.15\pm 3.21\) & \(47.52\pm 3.24\) & \(54.75\pm 4.03\) \\  & & TCGA & \(83.88\pm 1.72\) & \(78.42\pm 2.91\) & \(82.27\pm 2.61\) \\ \hline \multirow{3}{*}{U-Net++ [15; 16]} & \multirow{3}{*}{\(\times\)} & CVC-ClinicDB & \(68.89\pm 6.87\) & \(66.18\pm 7.40\) & \(71.61\pm 5.68\) \\  & & VIP-CUP18 & \(54.66\pm 3.77\) & \(49.89\pm 5.47\) & \(55.87\pm 2.23\) \\  & & TCGA & \(84.54\pm 1.62\) & \(79.47\pm 2.45\) & \(82.50\pm 2.42\) \\ \hline \multirow{3}{*}{U-Net+scAG (Proposed)} & \multirow{3}{*}{\(\times\)} & CVC-ClinicDB & \(71.72\pm 6.81\) & \(69.81\pm 8.97\) & \(74.21\pm 4.56\) \\  & & VIP-CUP18 & \(55.89\pm 3.97\) & \(51.41\pm 6.81\) & \(85.24\pm 1.82\) \\ \cline{1-1}  & & TCGA & \(85.18\pm 1.31\) & \(81.74\pm 1.89\) & \(83.84\pm 3.33\) \\ \hline \multirow{3}{*}{U-Net+scAG (Proposed)} & \multirow{3}{*}{✓} & CVC-ClinicDB & \(\mathbf{73.31\pm 5.85}\) & \(\mathbf{72.43\pm 7.14}\) & \(\mathbf{76.82\pm 2.94}\) \\ \cline{1-1}  & & VIP-CUP18 & \(\mathbf{56.26\pm 3.51}\) & \(\mathbf{51.79\pm 4.42}\) & \(57.11\pm 3.54\) \\ \cline{1-1}  & & TCGA & \(\mathbf{85.83\pm 1.41}\) & \(\mathbf{83.74\pm 1.81}\) & \(\mathbf{83.86\pm 2.46}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results on three datasets (best results marked bold). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\
\begin{tabular}{c} **Deep** \\ **Supervision** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **No.** \\ **of Parameters** \\ \end{tabular} } & \multicolumn{4}{c}{**Inference Time(ms/img)**} \\ \cline{4-7}  & & & **CVC-ClinicDB** & **VIP-CUP18** & **TCGA** \\ \hline U-Net [11] & \(\times\) & \(8637\times 10^{6}\) & 15 & 15 & 14 \\ Attention U-Net [17; 18] & \(\times\) & \(8726\times 10^{6}\) & 22 & 21 & 21 \\ U-Net++ [15; 16] & \(\times\) & \(10,198\times 10^{6}\) & 28 & 26 & 26 \\ U-Net+scAG (Proposed) & \(\times\) & \(8656\times 10^{6}\) & 33 & 32 & 32 \\ U-Net+scAG (Proposed) & ✓ & \(8804\times 10^{6}\) & 34 & 33 & 34 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of latency of testing consumption on three datasets. Dice score of 89.97%, 90.99% as compared to 91.92% of our proposed method, their segmentation results still have worse shapes than ours when comparing with ground truth. For the brain example of the TCGA dataset, standard U-Net seems to poorly under-segment the tumor. Attention U-Net though has a good shape in its prediction, but it still lacks semantic information to fully predict tumor. The predicted result is almost identical to the ground truth by enhancing the feature representation using scAG.