# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
## 4 Experimental Results and Discussion
### Nodule Segmentation  
The proposed lung nodule segmentation model was compared to the state-of-the-art approaches and evaluated in terms of quantitative and qualitative results. For the quantitative study, we used ACC, SEN, and SPE for pixel-level and DSC and IoU for object-level performance, respectively, as shown in Table 3. We compared the AWEU-Net to six different lung nodule segmentation models considering both datasets: PSPNet [40], MANet [41], PAN [42], FPN [43], DeeplabV3 [44], and U-Net [21,22]. As shown in Table 3, the integration of both PAWE and CAWE with the U-Net outperformed the segmentation results of the baseline model (U-Net). In general, AWEU-Net outperforms all tested models in terms of the ACC, SPE, DSC, and IoU metrics on the LUNA16 dataset. AWEU-Net yields ACC, SPE, DSC, and IoU scores of 91.32%, 93.46%, 89.79%, 82.32%, and 89.88%, respectively, which is 1.18%, 1.47%, 0.97%, 1.8%, and 0.93% points higher than the scores of the second-best method (i.e., U-Net). In turn, the DeeplabV3 achieved a SEN score of 93.01%, which is 1.32% points higher than AWEU-Net. However, the proposed segmentation model provides a comparable SEN score of 91.69%. In addition, using the test set of the LUNA16 and LIDC-IDRI datasets, the box plots of DSC and IoU scores of the six models and AWEU-Net were drawn to demonstrate the segmentation ability of AWEU-Net as shown in Figure 7. On both datsets, the proposed AWEU-Net yields higher DSC and IoU mean scores and the lowest standard deviation with only two outliers; this is as compared to the other six segmentation models, which contain many outliers with lower mean and higher standard deviation scores. Furthermore, to predict the probability of the binary segmented masks, the ROC and PR curves were constructed as shown in Figure 8. Using the LUNA16 test set, the proposed AWEU-Net model yields the highest AUC and PR of 97.10%, and 96.66%, respectively, among the seven segmentation models tested. On the other hand, AWEU-Net outperforms all the tested models in terms of all evaluation metrics on the LIDC-IDRI dataset. The proposed model yields ACC, SEN, SPE, DSC, and IoU scores of 94.66%, 90.84%, 96.41%, 90.35%, and 83.21%, respectively. It has improved by 0.3%, 1.16%, 0.06%, 0.48%, and 1.21% in ACC, SEN, SPE, DSC, and IoU scores from the original U-Net. Again, the box plots of DSC and IoU scores of the LIDC-IDRI dataset to compare the models' performance is displayed in Figure 9. Likewise, the proposed AWEU-Net achieved the highest DSC and IoU mean scores and the smallest standard deviation with only one outlier. The proposed model achieved an AUC of the \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Datasets** & **Models** & **ACC** & **SEN** & **SPE** & **DSC** & **IoU** \\ \hline \multirow{8}{*}{LUNA16} & PSPNet & 0.8718 & 0.8711 & 0.9012 & 0.8512 & 0.7513 \\  & MANet & 0.8874 & 0.8686 & 0.9285 & 0.8663 & 0.7743 \\  & PAN & 0.8604 & 0.8709 & 0.8873 & 0.8424 & 0.7354 \\  & FPN & 0.8846 & 0.9143 & 0.8905 & 0.8722 & 0.7806 \\  & DeeplabV3 & 0.8918 & **0.9301** & 0.8910 & 0.8794 & 0.7916 \\  & U-Net (baseline) & 0.9014 & 0.9136 & 0.9199 & 0.8882 & 0.8054 \\  & **Proposed\_AWEU-Net** & **0.9132** & 0.9169 & **0.9346** & **0.8979** & **0.8234** \\ \hline \multirow{8}{*}{LIDC-IDRI} & PSPNet & 0.9309 & 0.8514 & 0.9620 & 0.8684 & 0.7783 \\  & MANet & 0.9327 & 0.8749 & 0.9557 & 0.8788 & 0.7905 \\ \cline{1-1}  & PAN & 0.9268 & 0.8369 & 0.9603 & 0.8577 & 0.7653 \\ \cline{1-1}  & FPN & 0.9393 & 0.8981 & 0.9562 & 0.8934 & 0.8127 \\ \cline{1-1}  & DeeplabV3 & 0.9429 & 0.9023 & 0.9602 & 0.8983 & 0.8191 \\ \cline{1-1}  & U-Net (baseline) & 0.9436 & 0.8968 & 0.9635 & 0.8987 & 0.8200 \\ \cline{1-1}  & **Proposed\_AWEU-Net** & **0.9466** & **0.9084** & **0.9641** & **0.9035** & **0.8321** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between the proposed AWEU-Net and six other models on the LIDC-IDRI and LUNA16 test datasets (bold represents the best performance).