RETRACTED ARTICLE: Periapical dental X-ray image classification using deep neural networks

Abstract

This paper studies the problem of detection of dental diseases. Dental problems affect the vast majority of the world's population. Caries, RCT (Root Canal Treatment), Abscess, Bone Loss, and missing teeth are some of the most common dental conditions that affect people of all ages all over the world. Delayed or incorrect diagnosis may result in mistreatment, affecting not only an individual's oral health but also his or her overall health, thereby making it an important research area in medicine and engineering. We propose a pipelined Deep Neural Network (DNN) approach to detect healthy and non-healthy periapical dental X-ray images. Even a minor enhancement or improvement in existing techniques can go a long way in providing significant health benefits in the medical field. This paper has made a successful attempt to contribute a different type of pipelined approach using AlexNet in this regard. The approach is trained on a large dataset of 16,000 dental X-ray images, correctly identifying healthy and non-healthy X-ray images. We use an optimized Convolutional Neural Networks and three state-of-the-art DNN models, namely Res-Net-18, ResNet-34, and AlexNet for disease classification. In our study, the AlexNet model outperforms the other models with an accuracy of 0.852. The precision, recall and F1 scores of AlexNet also surpass the other models with a score of 0.850 across all metrics. The area under ROC curve also signifies that both the false-positive rate and false-negative rate are low. We conclude that even with a big data set and raw X-ray pictures, the AlexNet model generalizes effectively to previously unseen data and can aid in the diagnosis of a variety of dental diseases.

CVApool: using null-space of CNN weights for the tooth disease classification

“…Table 5 clearly shows that most studies have shown the benefits of using pre-trained CNN models. Research on the teeth disease identification has been mostly restricted to limited comparisons of simulating the transfer learning methodology on some sort of CNN types, such as AlexNet [37] and VGG16 [36]. Some studies present an effort for the use of custom CNN models, such as CustomAlexNet [17], a fully convolutional network (FCN) [23] and a hybrid neural network (HNN) [8].…”

“…In recent studies, researchers have focused on validating the efficiency and accuracy of deep learning algorithms for analyzing intraoral X-ray images. After the development of CNNs, deep learning architectures for dental imaging evolved as the variations in CNNs, such as VGGNet [36], GoogLeNet [21], AlexNet [37], EfficientNet [38] and DenseNet [39]. For example, Lee et Convolutional neural networks (CNNs) are a type of artificial neural network that has been specifically developed to handle signals, sequences, images or volumetric data.…”

Deep Recurrent Neural Network-Based Assessment of Human Dental Age and Gender from Dental Radiographs

“…For accurate age estimates based on developmental changes, recurrent neural networks (RNNs) are particularly useful for identifying temporal dependencies in dental imaging sequences. Additionally, generative adversarial networks (GANs) have demonstrated potential in synthesizing high-quality dental pictures, assisting in data augmentation, and resolving issues related to small datasets [5]. Transfer learning methods have also improved efficiency during dental imaging tasks.…”

Value co-destruction causing customers to stop service usage: a topic modelling analysis of dental service complaint data

“…The data used for the analysis were descriptive complaint questionnaires filled out by dental clinic patients in Japan. Although oral diseases are largely preventable, they affect the majority of the world's population, approximately 3.5 billion (Vasdev et al, 2022;World Health Organization, 2022). This means that oral health is an indispensable issue for customers.…”