## 4 Experimental Results
### Experiment and Result  
In the first stage of the experimental result, we define the optimal layers in each deep pre-trained network that best represents the problem. Table 3 explains the features set extracted from each deep pre-trained network respective to their layer. The extracted features set are tested with a support vector machine model to reach the final classification result. There is no reference for choosing the layer in each network; therefore, we tried several layers before its prediction layer. Some of them show that the best layers are the pooling layer, whereas others may choose the layer before. The highest performance can be reached from the \"68\" layer in the VGG16 model, which presents an accuracy of 90.57%, sensitivity of 91.30%, and specificity of 90%. Furthermore, Resnet50, Resnet101, and Xception also show a very promising result of around 88% accuracy. Noticeably, none of the deep activated features have less than 80% accuracy, proving that deep activated features are effective. To enhance the performance so far, we combined each deep activated feature set with geometric features and fed them into the SVM model (Table 4). The result of the fusion feature shows that the fusion Xception feature evolved. After the combination, Figure 5 shows that the fusion features of the Xception network have become the most prominent features and improved the performance to 90.45%, 100%, and 86.67% for accuracy, sensitivity, and specificity, respectively. The highest difference is the improvement of sensitivity from 91% to 100%; therefore, the fusion Xception features set has demonstrated geometric contribution in the proper combination with deep activated features. Although the performance is compatible with Xception fusion features, Resnet18 and Googlenet also show an improvement of 83.02% to 86.79% accuracy and 84.91% to 88.68% accuracy, respectively. Noticeably, none of the fusion feature sets have lower accuracy than their respective deep activated features. In conclusion, fusion features show an obvious advantage on deep activated features set alone. We randomly divided the training and testing set for cross-validation to design and evaluate the appropriate caries detection method. The k-fold cross-validation is a well-known reliable technique to test the robustness of the method. The application of k-fold cross validation proves the proposed method's reliability to cover the whole problem and adapt to the unknown samples; this technique was also used to prevent the overfitting of the method on our testing data. We then used the most prominent results obtained by the different classification models to determine which classification model best fit the features set. In this study, decision tree (DT), k-nearest neighbor (KNN), Naive Bayes (NB), random forest (RF), and support vector machine were used (Table 5). In this step, we also applied k-fold cross-validation to prevent the overfitting of the method and to calculate the final average assessment. The support vector machine is obviously the most dominant model that shows an accuracy of 91.70%, sensitivity of 90.43%, and specificity of 92.67%. As mentioned earlier in Section 3.1, the used dataset is considered balanced and shows a small difference in the number of caries and non-caries samples; therefore, precision (also known as positive predictive value) and recall pair (also known as sensitivity) also show promising values of 91.51% and 90.43%, respectively. Finally, we generated the receiver operating characteristic(ROC) curves for each classifier; the ROC curves describe each classifier in each fold of the experi