# Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation
## 4 Experiments and Results
### Results  
#### 4.3.1 Skin Cancer Segmentation  
We used the Adam optimizer with a learning rate of \(3\times 10^{-4}\). Besides, epoch and batch size were set to 4 and 150 respectively. The training time of the proposed model was about 10 h.  
Table 1 summarizes the quantitative results for the comparison between this experiment and other methods. We tried different sets of hyperparameters for the optimization of the Residual-Attention UNet++ model. Hyperparameters tuning was conducted manually by training the model with different sets of hyperparameters and evaluating their results; Table 2 lists the hyperparameters adjusted during the experiment. The results of Residual-Attention UNet++ against other methods with respect to F1-score, SE, IoU, and DC are presented in Table 1, which shows that the proposed model achieved the highest results for the task of skin cancer segmentation. Especially, the proposed model increased the F1-score by 0.66%, the SE by 1.35%, the IoU by 0.64%, and the DC by 0.75% over UNet++.  
Figure 11 shows some of the example outputs from the testing phase. As observed in Figure 11, the target lesions were segmented accurately with almost the same shape of ground truth in most cases. In addition, it can be clearly seen that the input image in the second row contains two spots; the Residual-Attention UNet++ still segmented the desired target accurately. Moreover, if we examine the third and fourth rows in Figure 11, the UNet++ and Residual-Attention UNet++ predicted two lesions and one lesion respectively, while ground truth only had one, which shows the robustness of the proposed model. In addition, Figure 12 shows the comparison between Residual-Attention UNet++ and other  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **F1-Score** & **SE** & **IoU (\%)** & **DC (\%)** \\ \hline FCN & 0.8671 & 0.8748 & 81.45 & 86.58 \\ SegNet & 0.8862 & 0.8801 & 81.58 & 87.81 \\ U-Net & 0.8880 & 0.8884 & 81.49 & 87.79 \\ Attention UNet & 0.8866 & 0.8965 & 81.46 & 87.81 \\ UNet++ & 0.8896 & 0.8991 & 81.79 & 87.93 \\ Attention UNet++ & 0.8938 & 0.8942 & 82.21 & 88.54 \\ Residual Attention UNet++ & 0.8955 & 0.9112 & 82.32 & 88.59 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental performance of Residual-Attention UNet++ and other methods on skin datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & \\ \hline Batch size & 64, 32, 16, 8, 4 \\ Learning rate & 0.0001, 0.0003 \\ Optimizer & Adam, SGD \\ Lr scheduler & ExponentialLR, StepLR \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters adjusted in the experiment.  
methods on F1-score after multiple experiments, which further illustrates the robustness of the proposed model.  
Figure 11: Qualitative assessment of UNet++ and Residual-Attention UNet++ performance on skin cancer. First column: original image, second column: ground truth, third column: UNet++’s output, last column: Residual-Attention UNet++’s output.  
Figure 12: The comparison between Residual-Attention UNet++ and other methods on F1-score after multiple experiments for skin dataset.