## 2 Related Works  
In the dental examination using radiographs, caries can be recognized as a break in the tooth, parts missing from a tooth, or tooth loss. There is no obvious symptom or criteria on the shape, size, or intensity for tooth decay except the dentist's diagnosis experience, which causes a huge challenge for computer-aided diagnosis systems based on image processing. [PERSON] et al. (2018) proposed a method to detect tooth decay using a support vector machine (SVM) and a backpropagation neural network (BPNN). The method uses two features set separately for features extraction: Autocorrelation Coefficient and Gray-level Co-occurrence Matrix. Then, a model of SVM and BPNN was applied separately for classification purposes. The result shows that SVM has around 79% accuracy on the testing set, whereas BPNN is around 75% accuracy. The result is inefficient and needs more works for improvement. Besides this, in the article, the dataset's description is not mentioned; thus, it may lead to questions about the research's reliability. [PERSON] et al. (2018) tried to enhance the backpropagation neural network layer and features extraction of autocorrelation coefficient matrix. The method was tested on 80 private tooth images (55 images for training and 35 images for testing and shows 94% accuracy); however, there is a great computational burden when the number of layers in the backpropagation neural network is increased. In addition, effective measures, such as sensitivity (SEN), specificity (SPEC), precision (PRE), and F-measure, are not mentioned. Further, the pretty small testing data (35 images) without cross-validation also shows weakness, which cannot address the whole problem of tooth decay. [PERSON] (2018) proposed an intelligent system with dragonfly optimization. Multi-linear principal component analysis (MPCA) was applied to extract the feature set. The features set were then fed into a neural network classifier trained using an optimization method, which was the adaptive dragonfly algorithm (ADA). The proposed MPCA model non-linear Programming with ADA (MNP-ADA) was tested with 120 private tooth images divided into three test cases. Each test case consisted of 40 images, 28 images were used for training, and 12 images were used for testing. The other classifiers, such as fruit fly (FF) ([PERSON] et al., 2016) and grey-wolf optimization (GWO) ([PERSON] et al., 2016), and feature sets, such as linear discriminant analysis (LDA) ([PERSON] et al., 2016), principal component analysis (PCA) ([PERSON] et al., 2016), and independent component analysis (ICA) ([PERSON] et al., 2016), were also used in the testing for comparison. The final average results show that the MNP-ADA model reaches 90% accuracy, 94.67% sensitivity, and 63.33% specificity. The result shows a low performance of specificity, which describes non-caries patients misclassified as caries patients; therefore, the distinction between caries and non-caries patients is not efficient, so the performance needs to be improved. Because the result shows a high accuracy value despite a low specificity value, this may lead to hesitations about the balance of data between caries and non-caries images. This study also shows other measure values, such as precision and f1-score, which are discussed in more detail in the results section. Nowadays, deep learning makes a great breakthrough in the machine learning field ([PERSON] et al., 2017). The convolutional neural network (CNN) is the most well-known deep learning model, which could be used for many purposes, such as the detection of new unknown objects (transfer learning), fine-tuning the weight, or feature extraction ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018); however, so far as we are aware, there is no previous study that applied deep learning for caries classification problems, especially in dental radiographs; meaning that there may be a need for research in this area.