# Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models
## 1 Introduction
We believe that this work will help healthcare professionals and dental practitioners detect dental caries. Furthermore, it may serve for the appraisal and comparative analysis of emerging methodologies within this domain. Efforts to develop techniques for the cavity detection task have been previously described in the literature [11]. Cavity detection methods have focused on classifying teeth as decayed or normal. One of the early trials of the use of neural networks (NNs) to classify cavities was by [24]. They proposed a NNs-based tooth decay diagnostic system in which they used normalized auto-correlation coefficients to distinguish decayed teeth and normal teeth. The back propagation algorithm was used to construct the weights for the NN classifier in this system. Additionally, [PERSON] et al. [25] classified dental X-rays into normal or decayed teeth images. They proposed a system of detecting dental caries and classifying X-ray images using deep NNs. The deep NNs technique was based on a stacked sparse auto-encoder containing three hidden layers for features extraction and a Soft-Max classifier to classify the teeth images into decayed teeth or normal teeth. This approach showed good detection performance and obtained an accuracy score of 97%. Later, [PERSON] et al. [6] introduced the DL model based on a CNN for the detection and localization of dental lesions in Near-Infrared Light Transillumination (NILT) images. Their study assessed the model's performance in two key aspects: pixel-wise segmentation into tissue classes and binary labeling, and distinguishing between healthy and diseased regions of interest. The results demonstrated that employing a DL approach for analyzing dental images yielded favorable outcomes, providing valuable support for dental practitioners [6]. Similarly, [PERSON] et al. [26] applied deep CNNs to identify caries lesions in NILT images. They conducted training using two different CNN architectures, Resnet18 and Resnet50, and validated the model's performance through a 10-fold cross-validation process. The findings indicated that both models performed comparably in predicting caries in tooth segments within NILT images. Notably, Resnet50 exhibited slightly superior performance, as measured by the area under the receiver operating characteristic curve (AUC) [26]. [PERSON] et al. [27] proposed a network for detecting caries in panoramic dental X-rays (PaXNet), which is composed of two main modules: the feature extraction module and the classification module. In the feature extraction block, three pretrained models were used: Encoder, CheXNet, and InceptionNet, where SoftMax is used as a classifier. [PERSON] et al. [28] used DL for the detection of caries lesions and the classification of radiographic extensions on panoramic films. The dataset consisted of 1160 panoramic films. A CNN NNU-Net was used in the detection process, and DenseNet-121 was used for the classification step according to their depths. The accuracy and recall rate of NNU-Net were 0.986 and 0.821, respectively. [PERSON] et al., in their study, [29] successfully identified approximal dental caries in bitewing images and sorted them based on the condition of the lesion. They conducted classification model evaluations employing Inception and ResNet architectures, considering three different learning rates: 0.1, 0.01, and 0.001. [PERSON] et al. [30] used oral photographs to detect and classify cavities as caries-free, non-cavitated caries lesions, or caries-related cavitation. To train AI methods cyclically and repeatedly, each expert diagnosis was used as a reference standard. Currently, deep learning (DL) methods have gained popularity among researchers due to their exceptional performance and increased accuracy when trained with substantial amounts of data. In the study by [PERSON] et al. [31], the effectiveness of deep CNN algorithms to detect dental caries in periapical X-ray images was examined. They employed a pre-trained GoogLeNet Inception v3 CNN network for pre-processing and transfer learning.