Dental caries is one of the oral diseases which are a major health problem for many people across the globe. It can lead to pain, discomfort, disfigurement, and even death in some cases. Dental caries is caused by the infection of the calcified tissue of the teeth. They can be prevented easily by early diagnosis and treated in the early stages. The development of a reliable model for the diagnosis and classification of dental caries can lead to effective and timely treatment. The G.V Black Classification system of dental caries is one of the systems which is widely accepted worldwide. It classifies caries into six classes based on the location of caries. This paper proposes a novel deep convolution layer network (CNN) with a Long Short-Term Memory (LSTM) model for the detection and diagnosis of dental caries on periapical dental images. The proposed model utilizes a convolutional neural network for extracting the features and Long Short term memory (LSTM) for conducting short-term and long-term dependencies. The main objective of this study is to detect dental caries and classify them into various classes based on G.V Black Classification. The periapical dental images are pre-processed and are fed as input to deep convolutional neural networks. The deep convolutional neural network classifies the input into various classes. The proposed algorithm is optimized using the Dragonfly optimization algorithm and gave an accuracy of 96%. Experiments are conducted to evaluate and compare the proposed model with the recent state-of-art deep learning models. This study justifies that a deep convolutional neural network is one of the most efficient ways to detect and classify dental caries into various G.V black classes. The achieved accuracy of the proposed optimal CNN-LSTM model for G.V black classification proves its efficacy as compared to the classification accuracy achieved by widely used pre-trained CNN models i.e. Alexnet (accuracy: 93%) and GoogleNet (accuracy: 94%) on the same database. The performance of the proposed CNN-LSTM model is further strengthened by comparing the results with the CNN model, 2 layer LSTM model and CNN-LSTM model without dragonfly optimization. The proposed optimal CNN-LSTM model shows the best performance with 96% accuracy and helps in dental image classification as the second opinion to the medical expert.

龋齿是口腔疾病之一，其是全球许多人的主要健康问题。在某些情况下，它可能导致疼痛，不适，毁容甚至死亡。龋齿是由牙齿钙化组织的感染引起的。通过早期诊断可以很容易地预防它们，并在早期进行治疗。建立可靠的龋齿诊断和分类模型可以导致及时有效的治疗。龋齿的GV黑色分类系统是世界范围内广泛接受的系统之一。根据龋齿的位置将其分为六类。本文提出了一种新颖的具有长短期记忆（LSTM）模型的深度卷积层网络（CNN），用于在根尖周图像上检测和诊断龋齿。提出的模型利用卷积神经网络提取特征，并利用长期短期记忆（LSTM）进行短期和长期依赖性。这项研究的主要目的是检测龋齿，并根据GV黑色分类将其分类为各种类别。根尖周图像被预处理并作为输入输入到深度卷积神经网络。深度卷积神经网络将输入分为各种类别。所提出的算法使用蜻蜓优化算法进行了优化，其准确度达到96％。进行了实验，以评估提出的模型并将其与最新的深度学习模型进行比较。这项研究证明深层卷积神经网络是检测龋齿并将其分类为各种GV黑色类别的最有效方法之一。与通过广泛使用的预先训练的CNN模型（即Alexnet（准确性：93％）和GoogleNet（准确性：94％））获得的分类准确性相比，所提出的针对GV黑色分类的最佳CNN-LSTM模型达到的准确性证明了其有效性。相同的数据库。通过将结果与CNN模型，两层LSTM模型和没有蜻蜓优化的CNN-LSTM模型进行比较，可以进一步增强所提出的CNN-LSTM模型的性能。所提出的最佳CNN-LSTM模型以96％的准确性显示出最佳性能，并有助于医学专家对牙科图像进行分类。与通过广泛使用的预先训练的CNN模型（即Alexnet（准确性：93％）和GoogleNet（准确性：94％））获得的分类准确性相比，所提出的针对GV黑色分类的最佳CNN-LSTM模型达到的准确性证明了其有效性。相同的数据库。通过将结果与CNN模型，两层LSTM模型和没有蜻蜓优化的CNN-LSTM模型进行比较，可以进一步增强所提出的CNN-LSTM模型的性能。所提出的最佳CNN-LSTM模型以96％的准确性显示出最佳性能，并有助于医学专家对牙科图像进行分类。与通过广泛使用的预先训练的CNN模型（即Alexnet（准确性：93％）和GoogleNet（准确性：94％））获得的分类准确性相比，所提出的针对GV黑色分类的最佳CNN-LSTM模型达到的准确性证明了其有效性。相同的数据库。通过将结果与CNN模型，两层LSTM模型和没有蜻蜓优化的CNN-LSTM模型进行比较，可以进一步增强所提出的CNN-LSTM模型的性能。所提出的最佳CNN-LSTM模型以96％的准确性显示出最佳性能，并有助于医学专家对牙科图像进行分类。94％）。通过将结果与CNN模型，两层LSTM模型和没有蜻蜓优化的CNN-LSTM模型进行比较，可以进一步增强所提出的CNN-LSTM模型的性能。所提出的最佳CNN-LSTM模型以96％的准确性显示出最佳性能，并有助于医学专家对牙科图像进行分类。94％）。通过将结果与CNN模型，两层LSTM模型和没有蜻蜓优化的CNN-LSTM模型进行比较，可以进一步增强所提出的CNN-LSTM模型的性能。所提出的最佳CNN-LSTM模型以96％的准确性显示出最佳性能，并有助于医学专家对牙科图像进行分类。