## 4 Experimental Results
### Experiment and Result
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Network** & **Alexnet** & **Googlenet** & **VGG16** & **VGG19** & **Resnet18** & **Resnet50** & **Resnet101** & **Xception** \\ \hline ACC & 0.8679 & 0.8679 & 0.9057 & 0.8113 & 0.8868 & 0.8868 & 0.8868 & **0.9245** \\ SEN & 0.7826 & 0.8696 & 0.9130 & 0.7826 & 0.8696 & 0.8696 & 0.8261 & **1.0000** \\ SPEC & 0.9333 & 0.8667 & 0.9000 & 0.8333 & 0.9000 & 0.9000 & **0.9333** & 0.8667 \\ PPV & 0.9000 & 0.8333 & 0.8750 & 0.7826 & 0.8696 & 0.8696 & **0.9048** & 0.8519 \\ NPV & 0.8485 & 0.8966 & 0.9310 & 0.8333 & 0.9000 & 0.9000 & 0.8750 & **1.0000** \\ F1-score & 0.7200 & 0.7407 & 0.8077 & 0.6429 & 0.7692 & 0.7692 & 0.7600 & **0.8519** \\ AUC & 0.9087 & 0.8949 & 0.9594 & 0.8659 & 0.9123 & 0.9580 & 0.9087 & **0.9688** \\ \hline \hline \end{tabular}  
* The highest performance for each measured factor regarding to network was highlighted in bold.  
\end{table} 
Table 4: Performance of fusion features corresponding to networks. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Network** & **Alexnet** & **Googlenet** & **VGG16** & **VGG19** & **Resnet18** & **Resnet50** & **Resnet101** & **Xception** \\ \hline Layer & fc8 & pool5-7x7\_s1 & fc8 & fc8 & pool5 & avg\_pool & pool5 & avg\_pool \\ \hline ACC & 0.8679 & 0.8302 & **0.9057** & 0.8113 & 0.8491 & 0.8868 & 0.8868 & 0.8868 \\ SEN & 0.7826 & 0.8261 & **0.9130** & 0.7391 & 0.8261 & 0.8696 & 0.8261 & **0.9130** \\ SPEC & **0.9333** & 0.8333 & 0.9000 & 0.8667 & 0.8667 & 0.9000 & **0.9333** & 0.8667 \\ PPV & 0.9000 & 0.7919 & 0.8750 & 0.8095 & 0.8261 & 0.8696 & **0.9048** & 0.8400 \\ NPV & 0.8485 & 0.8621 & **0.9310** & 0.8125 & 0.8667 & 0.9000 & 0.8750 & 0.9286 \\ F1-score & 0.7200  
Figure 6: Comparison of the ROC curves for five classifiers. (**a**) Decision tree, (**b**) K-nearest neighbor, (**c**) Naïve Bayes, (**d**) Random Forest, (**e**) Support vector machine, and (**f**) Comparison of mean of receiver operating characteristic (ROC) curves for each classifier. For a comprehensive assessment, the execution time of the proposed caries detection method is also computed. The experiments were conducted using the Matlab2020a environment in Windows 10. The main process was performed using a CPU core i7-9750 HF, supported by a GeForce GTX 2060 graphic card. Each function process is carefully considered since they are factors used to determine the complexity of the method. Table 6 shows that the total process takes 13.79 s in total, and the most complex function, which is deep activated feature extraction, takes less than 10 s. Also, the geometric feature calculation was extracted smoothly in only 2.52 s. Based on these results, we consider the proposed method to perform considerably well and capable of wide implementation, even with low computer specifications. Based on the prediction and evaluation time, an image of a tooth after segmentation will take only 0.28 s (less than 1 s) to know if it is caries or non-carries. This is optimal for dentists, even in a large hospital with a huge number of patients. Lastly, the proposed method was compared with the previous state-of-the-art techniques (Table 7). Because the different methods were conducted in different datasets, each dataset's size and complexity will make a difference in performance. For a fair comparison, we detail the method and describe the difference and the advantages/disadvantages of each method. In addition, because some methods are not fully described but have been tested on other datasets in other papers, we provide a reference to the appropriate study and provide a description. The comparison table shows that [11; 12] have a disappointing performance, whereas [13] performs much better; however, considering the accuracy of 90.00%, the sensitivity of 94.67, and specificity of 63.33%, we can see an imbalance in data as well as a low-performance result. Our proposed method achieved 92.67% specificity compared to the other methods, which is a 29.34% improvement, and the remaining sensitivity values are better than 90%.