# Adaptive Aggregated Attention Network for Pulmonary
## 4 Results and Discussions
### Ablation Experiment
#### 4.2.4 The Overall Architecture  
We combine MIXUP and the various attention networks separately and test them on the dataset. Based on Table 3, we can find that, when the MIXUP operation is combined with different attention networks, the classification performance of the model will be improved. Especially, the accuracy of classification increases a lot when the MIXUP operation is combined with RAN. This represents that the spatial attention features of multi-resolution is more effective in diagnosing negative nodules than positive nodules. When MIXUP is combined with RAN and MSAN, the sensitivity increases a lot. This represents that the multi-scale attention network is effective in capturing the spatial fine-grained features of positive nodules. When the MIXUP operation is combined with MSAN, the accuracy increases a bit but sensitivity decreases a lot, which is similar to the combination of MIXUP and SE. When the three attention modules are combined with MIXUP at the same time, the model's performance is best. Based on the different combinations, we can find that the features extracted by different attention modules are complementary. When MIXUP, RAN, MSAN, and SE are all added into a baseline model DPN, the classification performance is best. Therefore, we aggregated the above three attention modules into our model and proposed an overall architecture (DPN + MIXUP + RSA + MSAN + SE), which is shown as Figure 2. The RSA module was utilized to extract different spatial attention features with different resolutions. Based on the different spatial attention features extracted, the MSAN module was utilized to extract multi-scale features and to fuse them effectively. Because of the advantages of the residual connection in the RSA module, we could apply the RSA and MSAN modules in different layers of the network, which is important when capturing different structural features and semantic features, and this operation does not cause overfitting. At the end of the network, the SE module was utilized to capture attention features between channels and to screen important channels. The proposed architecture was tested on the dataset, and the results are shown in Table 3. Based on the results, our proposed architecture is proven to be effective for nodule classification.  
To evaluate the effect of the threshold, we recorded the changes in F1-score under different thresholds, which are shown in Figure 7. When the threshold is less than 0.5, the value of the F1-score increases with the increase in threshold, but when the threshold is greater than 0.5, the F1-score decreases with the increase in threshold. Therefore, we set the threshold as 0.5 in our experiments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **ACC** & **TPR** \\ \hline DPN & 90.22\% & 81.1\% \\ DPN + MIXUP & 91.30\% & 91.89\% \\ DPN + RAN & 90.22\% & 94.59\% \\ DPN + MSAN & 91.30\% & 91.89\% \\ DPN + SEN & 90.22\% & 89.19\% \\ DPN + MIXUP + RAN & 93.48\% & 89.19\% \\ DPN + MIXUP + MSAN & 92.39\% & 83.78\% \\ DPN + MIXUP + SEN & 91.30\% & 86.49\% \\ DPN + MIXUP + RAN + MSAN & 92.39\% & 97.29\% \\ DPN + MIXUP + RAN + MSAN + SEN & 94.57\% & 89.19\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments on the 5 th fold.