## 3 Experiments
### Results  
Extracting discriminative features from images is a crucial step in this study. Therefore, we first verified the performance of our approach without considering the dependency between lesions. Table 3 compares our CNN encoders based on VGGNet (ours-vgg) and Resnet (ours-res) with the methods proposed in the last three years by [PERSON] et al. [6], [PERSON] et al. [39] and [PERSON] et al. [40] under AUC value for multi-classification problem of ChestX-ray14 dataset, where bold numbers indicate the best AUC value in each row. Among them, [PERSON] et al. [40] claimed that their dataset is assigned in the same way as [PERSON] et al. [6]. Table 3 shows that our best average AUC value is 0.791. In addition, our method achieves better results in detecting lesions such as atelectasis, effusion, infiltration, etc. As the research objective of this work, the whole model's ability of detecting lesion sequences was the focus of our validation. Table 4 compares the recall, precision, and F-score value of the proposed approaches to other methods, where bold numbers indicate the best AUC value in each column. CXNet-m2-b [41] is a model for image-based detection of multiple chest lesions proposed in this year. [PERSON], [PERSON] et al. [42] proposed a classical CNN-RNN model which we call SAT here and trained again using our dataset. Vggc4-CAL, Vggc5-CAL, Resb3-CAL, and Resb4-CAL mean the visual features are trained from conv4 layer of Vggnet, conv5 layer of Vggnet, block3 layer, and block4 layer of Resnet, respectively. 'Improvements-1' shows the improvements of our best model from CXNet-m2-b. 'Improvements-2' shows the improvements of our best model from SAT. CLA means the CLA network is used to take advantage of the interdependencies between lesions. Figure 2, generated using gephi software, shows the CLA network built by Algorithm 1 and correlation intension between two lesions are labeled as edge weights. The thickness of the edges rather than the length also reflects the strength of the association between the two lesions. The thicker is the edge, the stronger is the association. According to different combination of lesions in ChestX-ray14 dataset, different subgraphs are detected, as shown in Figure 2. Algorithm 2 was used to process these subgraphs and output updated label orders. Table 5 shows the label sequence of some lesion combinations before and after the updates, whose lengths are \(C=3\) and \(C=4\). \"Original order\" refers to the order of lesion labels for single chest X-ray image in ChestX-ray14 dataset. \"Updated order\" refers to the order of lesion labels for single \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Setting** & \multicolumn{2}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F-Score**} \\ \hline  & **C=2** & **C=3** & **C=4** & **C=2** & **C=3** & **C=4** & **C=2** & **C=3** & **C=4** \\ \hline CXNet-m2-b [41] & – & – & 0.724 & – & – & 0.713 & – & – & 0.718 \\ SAT [42] & 0.666 & 0.657 & 0.632 & 0.666 & 0.657 & 0.636 & 0.666 & 0.657 & 0.634 \\ Resb3-CAL-wopt & 0.684 & 0.676 & 0.653 & 0.684 & 0.676 & 0.653 & 0.684 & 0.676 & 0.653 \\ Resb3-CAL & 0.730 & 0.726 & 0.717 & 0.728 & 0.726 & 0.717 & 0.729 & 0.726 & 0.717 \\ Resb4-CAL & 0.707 & 0.694 & 0.686 & 0.707 & 0.694 & 0.686 & 0.707 & 0.694 & 0.686 \\ Resb3-CAL-CLA & 0.733 & 0.730 & 0.728 & 0.733 & 0.730 & 0.728 & 0.733 & 0.730 & 0.728 \\ Vggc4-CAL & 0.719 & 0.708 & 0.691 & 0.719 & 0.708 & 0.691 & 0.719 & 0.708 & 0.691 \\ Vggc5-CAL & 0.739 & 0.734 & 0.729 & 0.739 & 0.734 & 0.729 & 0.739 & 0.734 & 0.729 \\ Vggc5-CAL-CLA & **0.739** & **0.739** & **0.741** & **0.739** & **0.737** & **0.741** & **0.739** & **0.738** & **0.741** \\ Improvements-1 & – & – & 0.017 & – & – & 0.028 & – & – & 0.023 \\ Improvements-2 & 0.073 & 0.082 & 0.109 & 0.073 & 0.080 & 0.105 & 0.073 & 0.081 & 0.107 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation and comparison of models on precision, recall, and F-score value.