## 4 Experiment and Discussion
### Comparison  
We conducted experiments on our dataset using another three methods, mentioned in Section 2.1, which were proposed by [PERSON] [18], [PERSON] [19], and [PERSON] [6]. The cross-validation settings used in these experiments were the same as in Section 4.3. The average accuracy and recall of these four methods are recorded in Figure 3.  
Most of traditional methods are designed based on the experience and ideas of the researchers. They are highly interpretable but not very accurate (or robust). As we can see from the results, though [PERSON]'s method had a high recall, it only used concavity information; which could easily misjudge the concave regions on the healthy tongue. Thus, the overall accuracy was low. [PERSON]'s method effectively improved the accuracy, but the recall is not guaranteed. These two methods both extract features manually and set thresholds that match the specific dataset. They don't have good generalization ability and fail to achieve a good balance between accuracy and recall. [PERSON] extracts tooth-marked tongue features using a VGG16 model, while we extract features using the model described in Section 3.2. For a more detailed comparison of these two methods, we provide the receiver operating characteristic  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **F2 Score** \\ \hline Fold\_1 & 76.0\% & 74.2\% & 73.8\% & 0.74 & 0.74 \\ Fold\_2 & 77.5\% & 74.4\%. & 75.6\% & 0.75 & 0.76 \\ Fold\_3 & 79.8\% & 79.0\% & 79.0\% & 0.79 & 0.79 \\ Fold\_4 & 82.2\% & 84.1\%. & 80.0\% & 0.82 & 0.81 \\ Fold\_5 & 77.5\% & 70.7\%. & 82.1\% & 0.76 & 0.80 \\ Average & 78.6\% & 76.5\% & 78.1\% & 0.77 & 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Five-fold cross-validation results.  
Figure 3: Comparison with other tooth-marked classification methods. [PERSON] and [PERSON] set thresholds based on concavity information, while [PERSON]’s and our methods extracted features using CNN. Orange bars represent the accuracy and gray bars represent the recall of these four methods.  
(ROC) curves in Figure 4. We also calculated the Area Under Curve (AUC) values of these two methods. [PERSON]'s method had an AUC of 0.81 and our method had an AUC of 0.85. These two methods both had stable performance, while our model used a shallower network and achieved better results.  
We provide two examples, showing the explanations from [PERSON]'s method and ours, in Figure 5. The left column is the original tongue image and the tooth-marked regions are provided by TCM practitioners which are marked by blue boxes. The middle column is the visualizations provided by [PERSON]'s method and the right column is the visualizations provided by our method. Although both of the methods classify the tongue image correctly, by generating Grad-CAM visualizations, we find that these two models are different in their attention. In the first row, the region on the left of the tongue has deceived [PERSON]'s method, but in our method, the true region of the teeth mark is successfully locked down. In the second row, our method can correctly find the regions of the teeth mark and ignore other irrelevant regions. [PERSON]'s method not only highlights the tooth-marked region but also highlights the non-toothed regions, such as the upper part of the tongue. Non-toothed regions are given a high weight, meaning that the model does not focus on the regions that have the greatest impact on the classification, which is why our method's classification accuracy is higher.  
Figure 4: ROC curves for [PERSON]’s method and our method. [PERSON]’s method has an AUC of 0.81 and our method has an AUC of 0.85.