## 5 Conclusions  
In this article, we present a caries detection method using radiography images. Firstly, the radiography images were manually defined by dentists as either caries or non-caries. Later, in the feature extraction process, tooth images were used to extract the deep activated feature. The proper layer used to extract deep activated features from each deep pre-trained model was defined during experiments. Then, the geometric feature was also extracted and combined with deep activated features to build fusion features. The optimal features set was explored by a performance comparison between deep activated features fusion features. The set of geometric features was reduced to its minimum while retaining the optimal information. Next, we fed the fusion into classification models such as support vector machine (SVM), decision tree (DT), k-nearest neighbor (KNN), Naive Bayes (NB), and random forest (RF) to classify between caries and non-caries images. Our proposed method has achieved 91.70%, 90.43%, and 92.67% for accuracy, sensitivity, and specificity, respectively. We improved the accuracy by 1.7%, from 90% to 91.70%, and the specificity by 29.34%, from 63.33% to 92.67%; the sensitivity was also good at 90.43% compared to previous state-of-the-art methods. The proposed method gives two key contributions: the first contribution is to find the best features set, which is the combination between deep activated features and geometric features, and then fit a proper classification model to describe the problem. The second contribution is to enhance the performance by improving the specificity measure factor. The performance of the deep activated feature is not proportional to the complexity or size of the model. The VGG16 deep activated feature is better than Xception, whereas the fusion result is the opposite. Our choice of the deep activated feature plays an important role; however, choosing analytically calculated features also contributed to the result equally. The finding of which deep activated feature features are compatible with analytically calculated features is more important than finding the best deep activated feature among all pre-trained models. While most research tries to build networks as deep as possible to improve the learning performance, our result proves that the performance is sometimes irrelevant to the network's depth. More importantly, the calculated feature's combination may play a key role in improving the performance and, therefore, unexchangeable for the pre-trained model's depth. The processing time, which takes 13.79 s for the whole experiment and 0.28 s for prediction, demonstrates that the method can be widely implemented in a low-tech computer for a trivial time-consuming. Nonetheless, despite the advantage compared with the previous state-of-the-art, this study's limitation is that we conducted the detection of caries based on the manually segmented teeth. In future work, we will improve our research as a fully automated system by performing automatic segmentation. We also have a great interest in extending our method to classify different caries stages by using three-dimensional approaches. By that, our system will be an adjunct tool for both experienced and junior dentists. [PERSON] and [PERSON] conceived and designed this study. [PERSON] performed the experiments, simulations, and original draft preparation of the paper. [PERSON] helped in experimenting and evaluating the results. [PERSON] reviewed and edited the paper. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of the Tokai University (protocol code 19212 and on March 6\({}^{\text{th}}\), 2020). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.