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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...
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Citations
... Various methods have been proposed to automate X-ray analysis, encompassing tooth detection [4], classification [5], segmentation [6], caries detection [7], osteoporosis diagnosis [8], and forensic identification [9]. These approaches are typically tailored for common dental X-ray types like periapical, bitewing, and panoramic. ...
Accurate tooth segmentation is of paramount importance in oral healthcare because it provides critical positional data for clinical diagnosis, orthodontic treatment, and surgical procedures. Despite the widespread use of convolutional neural networks (CNNs) in image segmentation, its limitations in collecting complete global context and long-range interactions are acknowledged. Although vision transformers show promise in understanding larger contextual information, they struggle to manage the spatial complexities of medical images. To tackle these issues, the proposed DenUnet leverages the strengths of both CNNs and transformers. It introduces a dual-branch encoder that simultaneously extracts edge and body information and a double-level fusion module for integrating multi-scale features. To ensure the fine fusing of edge and body information derived from the two mentioned encoders, we propose a local cross-attention feature fusion module to enhance feature fusion with accurate blending losses. Experimental results underscore the superior efficacy of DenUnet in comparison to existing methods. We achieved 95.4% accuracy and 92.7% F1-score on the DNS dataset, which is particularly evident in its ability to adeptly handle irregular boundaries of dental datasets. Code is available at https://github.com/Omid-Nejati/DenUnet
... For improving dental disease diagnosis and treatment plans, deep learning [7] has significantly influenced the processing of intraoral X-ray images in image processing [8], segmentation [9][10][11][12] and enhancements [13][14][15]. These developments integrating intraoral X-ray imaging with deep learning techniques enhance the precision of oral health condition recognition and detection, such as dental caries [16][17][18][19][20][21][22], implant [23], oriented tooth [24], restoration by filling [25] and dental material [26]. This study adapts deep learning techniques on a novel dataset to recognize and detect twenty categories as abscessed teeth, calculus, caries, cysts, dental bridges, dental crowns, extracted teeth, filling overhang, impacted wisdom teeth, implants, mesialized dentition, mixed dentition, periodontal bone loss, pulpitis, restoration by filling, retained root, screw-retained restoration, single-root canal treatment, two-root canal treatment and three-root canal treatment. ...
In light of current developments in dental care, dental professionals have increasingly used deep learning methods to get precise diagnoses of oral problems. Using intraoral X-rays in dental radiography is imperative in many dental interventions. Integrating deep learning techniques with a unique collection of intraoral X-ray images has been undertaken to enhance the accuracy of dental disease detection. In this study, we propose an alternative pooling layer, namely the Common Vector Approach Pooling technique, to address the constraints associated with average pooling in deep learning methods. The experiments are conducted on a large dataset, involving twenty different dental conditions, divided into seven categories. Our proposed approach achieved a high accuracy rate of 86.4% in identifying dental problems across the seven oral categories.
... For improving dental disease diagnosis and treatment plans, deep learning [7] has significantly influenced the processing of intraoral X-ray images in image processing [8], segmentation [9][10][11][12] and enhancements [13][14][15]. These developments integrating intraoral X-ray imaging with deep learning techniques enhance the precision of oral health condition recognition and detection, such as dental caries [16][17][18][19][20][21][22], implant [23], oriented tooth [24], restoration by filling [25] and dental material [26]. This study adapts deep learning techniques on a novel dataset to recognize and detect twenty categories as abscessed teeth, calculus, caries, cysts, dental bridges, dental crowns, extracted teeth, filling overhang, impacted wisdom teeth, implants, mesialized dentition, mixed dentition, ...
In light of current developments in dental care, dental professionals have increasingly used deep learning methods to get precise diagnoses of oral problems. Using intraoral X-rays in dental radiography is imperative in many dental interventions. Integrating deep learning techniques with a unique collection of intraoral X-ray images has been undertaken to enhance the accuracy of dental disease detection. In this study, we propose an alternative pooling layer, namely the Common Vector Approach Pooling technique, to address the constraints associated with average pooling in deep learning methods. The experiments are conducted on a large dataset, involving twenty different dental conditions, divided into seven categories. Our proposed approach achieved a high accuracy rate of 86.4% in identifying dental problems across the seven oral categories.
... With regard to this information, it has both short and long term memory. To construct a LSTM layer, it consists of one memory state and three main gates of input, forget and output as shown in Figure 2. In order to sustain long terms dependency, the gate mechanism in LSTM network can decide which information to hold or leave [27]. ...
For traditional intrusion detection model, the system effectiveness is fully based on training dataset and feature selection. During feature selection, it needs more labour charge and trusted mainly on expert’s knowledge. Moreover, the training dataset contains more imbalanced data which in terms model tends to be biased. Here, an automatic approach is introduced to correct deficiency in the system. In this paper, the author proposes novel network anomaly detection (NID) build using categorical data. A model has to be designed with modified form of deep neural network primarily utilized for detecting anomaly within the network. Custom CNN-LSTM with Harris Hawks Optimization (named as custom optimized CNN-LSTM) is designed as a new classifier majorly used to detect the anomaly from word cloud to distinguish the data with effective performance. The experimental result shows that the proposed method achieves a promising output for network anomaly detection.
... In 2021, Singh and Sehgal [20] have employed CNN for feature extraction and "Long Short Term Memory (LSTM)" for performing the durable and the small reliance. The primary aim of the experiment was to identify the dental caries and categorize them into multiple classes. ...
... However, it has less generalization ability, and it is expensive because of the integration of models. By CNN-LSTM [20], the exact locations of the caries are identified in this approach. It utilizes the previously stored data for providing recommendations to dentists. ...
Dental caries is also referred as tooth decay, which is a common teeth disorder for humans. Based on the statistics, most of the adults faced this dental caries issue. It could result in disfigurement, discomfort, and pain. The issue of dental caries can be avoided effortlessly by early detection. The implementation of a reliable approach for the detection and classification of this issue; thus it is a timely and efficient treatment. However, the manual treatments are complex to identify which influences the clinical experiment. The inadequate history details of the patient also affect the proper treatment. On the other hand, the deep learning model has become the optimal choice when considering the evaluation of medical images. However, the traditional computerized approaches still have several limitations such as more resource consumption, and computational burden. Hence in this work, a deep learning-assisted caries segmentation and classification model is developed to treat the caries at the early stage to avoid the tooth loss. The dental caries images are collected from the standard “Panoramic Dental Dataset, and MLUA Caries Image Dataset”. Those images are given to the segmentation phase. The image segmentation process increases the accuracy, and robustness and minimizes the processing time and cost. The caries segmentation is performed using the Adaptive Trans-Dense Unet++ (ATDUnet++). This network effectively segments the given images and supports for producing high-quality outcomes. Here, the images are resized and normalized, and the noise in the images is removed. The parameters from ATDUnet++ are optimized by employing the Modified Running City Game Optimizer (MRCGO). The segmented images are given as the input to the classification phase. The classification of dental caries is done by using the proposed Vision Transformers-based Multi-scale Residual DenseNet with Gated Recurrent Unit (ViT-MRDGRU). This network categorizes dental caries precisely to prevent tooth loss and assists in making timely decisions for treatment. Throughout the result analysis, the accuracy and precision rate of the developed model are 96.59% and 96.55%. The final outcomes of the suggested model are justified by comparing it with other classifiers and algorithms.
... Short-term and long-term dependencies are assessed using an LSTM and a CNN in the developed framework. For identifying caries lesions of varying radiographic expansion upon bitewings, [23,24] used deep learning, hypothesizing it would be substantially better effective than individual dentists [24]. Assess the accuracy of a deep CNNbased Ai solution in identifying affected third molars in CBCT pictures. ...
This study introduces a novel approach for the segmentation and classification of tooth types in 3D dental models, leveraging the Adaptive Enhanced GoogLeNet (AEG) classifier and a Custom Mask Convolutional Neural Network (CM-CNN). Motivated by the complexities and potential misinterpretations in manual or semi-automatic tooth segmentation, our method aims to automate and enhance the accuracy of dental image analysis. The 3D dental models are processed using CM-CNN for segmentation, followed by classification using the AEG classifier. The significance of this work lies in its potential to provide a reliable and efficient solution for precise tooth categorization, contributing to improved diagnostic procedures and treatment planning in dentistry. Comparative analyses demonstrate the superiority of the proposed method, achieving high accuracy, precision, recall, and F-measure when compared to conventional techniques such as traditional geometrical transformation and deep convolutional generative adversarial networks (TD-model) and deep CNN (DCNN). The outcomes suggest the method's practical applicability in real-world dental scenarios, offering a promising contribution to the ongoing integration of AI in dentistry. This study employs a dataset of 3D dental models, and its significance lies in addressing the challenges associated with tooth segmentation and classification in dental images. Dental X-ray pictures play a crucial role in predicting dental disorders, and precise tooth segmentation is essential for effective diagnosis and treatment planning. Manual or semi-automatic segmentation processes are prone to misinterpretations due to image noise and similarities between tooth types. The proposed method, integrating CM-CNN and AEG, aims to automate this process, providing accurate tooth categorization. The significance of achieving this automation is notable in enhancing the overall efficiency of dental image analysis, contributing to quicker and more accurate diagnoses. The outcomes of the study showcase the method's superiority over conventional techniques, indicating its potential impact on improving dental diagnostic procedures and treatment strategies, ultimately benefiting both dental practitioners and patients.
... Tobacco usage is the prime implication for developing oral malignancies and oral leukoplakia. J Bánóczy et al. [38] have presented a relationship and consequences of excessive tobacco use. The main objective of this research is to showcase the relationship between oral leukoplakia and tobacco usage. ...
... In the proposed work, texture determination is done using a modified local binary pattern with three machine-learning classifiers: SVM, KNN, and CT. It has been observed that the MLBP method with SVM classifier outperforms the other techniques and guarantees the highest accuracy with different parameters [38][39][40][41][42][43][44][45]. ...
Oral cancer is the most extensive universal problem, with 10.3 million deaths in 2020 by the World Health Organization (WHO). It is the most common disease in developing countries. The conventional screening approaches for the early detection of oral malignancies are majorly dependent on medical professionals and their clinical experiences. Therefore, there is a massive requirement for automation in diagnosing cancerous and non-cancerous contusions in the oral cavity, which would cost a low price and provide an efficient prognosis in the early stage of the disease. This paper proposes a machine learning-based automated model using a modified local binary pattern (MLBP) to detect oral lesions in the oral cavity from OSCC (Oral Squamous Cell Carcinoma) images of 100x resolution and 400x resolution. The proposed MLBP provides superior spatial information that describes the connectivity in global and local regions of the OSCC images to depict the changes that occurred due to cancer in the oral cavity area. The performance of the MLBP is evaluated using a deep Convolution Neural Network (DCNN) to enahnce the feature distinctiveness of the features, and its effectiveness is compared with traditional Support Vector Machine (SVM), k-nearest Neighbor (KNN), and decision tree algorithms. The oral cancer images have been from the Mendeley dataset, which consists of 528 OSCC images and 696 normal epithelium oral cavity histopathological images of 400x resolution. The experiments have been conducted with different machine learning classifiers on both types of images. Results show that the MLBP SVM method incorporates the best performance for both image resolutions in accuracy, precision, recall, and F1-score performance metrics. The proposed MLBP-DCNN provides 91.36% and 94.44% accuracy for 100x and 400x resolution OSCC images, respectively, which has shown significant improvement over traditional techniques.
... As reported in the same study, the prevalence of caries in Indonesia, reaching 88.8%, surpassed the target set by the World Health Organization (WHO). 1,2 Dental caries is a condition where teeth are demineralized due to bacteria that produce acids as a result of carbohydrate fermentation and will colonize to form biofilms. 1,3,[4][5][6][7] Streptococcus mutans, a gram-positive bacterium, is identified as the primary culprit behind dental caries development in humans. Foods containing carbohydrates, especially sucrose, can accelerate the attachment of Streptococcus mutans to the enamel surface and will become dental plaque. ...
Streptococcus mutans is a bacterium involved in the formation of caries. Red ginger essential oil is extracted from rhizomes, has a content of natural compounds, and is used in medicine for antibacterial, anti-inflammatory, and anticancer. To prove the effect of red ginger essential oil on Streptococcus mutans bacteria. The agar diffusion method is performed to test antimicrobial activity and determine the Minimum Inhibitory Concentration (MIC) against Streptococcus mutans. Furthermore, the adherence test of Streptococcus mutans bacteria was carried out using a spectrophotometer l = 570nm. MIC red ginger essential oil against Streptococcus mutans bacteria at concentrations of 0.78% and 1.56%. In the 0.78% concentrate, an adherence value of 2.12 was obtained and in the 1.56% concentrate, an adherence value of 1.93 was obtained and 3.125% concentrate obtained an adherence value of 1.78. Red ginger essential oil has potential as an antimicrobial agent by inhibiting the adherence of Streptococcus mutans bacteria.
... o t is the output gate that decides which information shall be used for the output of the memory Fig. 5 The structure of a long short-term memory unit cell. The h t is the hidden state that calculates the memory cell output [35]. LSTM memory block is a complicated processing structure with several units. ...
Pneumonia is a serious inflammatory disease that causes lung ulcers, and it is one of the leading reasons for pediatric death in the world. Chest X-rays are perhaps the most commonly utilized modalities to recognize pneumonia. Generally, the illness could be analyzed by a specialist radiologist. But for some reason, the diagnosis may be subjective. Thus, the physicians must be guided by computer-aided diagnosis frameworks in this challenging task. In this study, we propose a combined deep learning architecture to identify pneumonia in chest radiography images. We first, use Adaptive Median Filter for images enhancement, then we employ a regularized Convolutional Neural Network for features extraction, and then we use Long Short Term Memory as a classifier. Finally, the attention mechanism is used to direct the network attention to relevant features. The suggested approach was tested on two publicly available pneumonia X-ray datasets provided by Kermany and the Radiological Society of North America. On the Kermany and RSNA datasets, the suggested technique attained accuracy rates of 99.91% and 88.86%, respectively. In the last stage of our experiments, we employed a Grad-CAM-based color visualization technique to precisely interpret the detection of pneumonia in radiological images. The results outperformed those of state-of-the-art approaches.
... In this study, the combination of statistics and neural networks can be seen which is an important feature. A combination of CNN and LSTM networks known as CNN-LSTM was suggested by Singh et al. [14] . The aim was to classify dental caries according to the G.V. black classes. ...
Background
Dental caries, also known as tooth decay, is a widespread and long-standing condition that affects people of all ages. This ailment is caused by bacteria that attach themselves to teeth and break down sugars, creating acid that gradually wears away at the tooth structure. Tooth discoloration, pain, and sensitivity to hot or cold foods and drinks are common symptoms of tooth decay. Although this condition is prevalent among all age groups, it is especially prevalent in children with baby teeth. Early diagnosis of dental caries is critical to preventing further decay and avoiding costly tooth repairs. Currently, dentists employ a time-consuming and repetitive process of manually marking tooth lesions after conducting radiographic exams. However, with the rapid development of artificial intelligence in medical imaging research, there is a chance to improve the accuracy and efficiency of dental diagnosis.
Methods
This study introduces a data-driven model for accurately diagnosing dental decay through the use of Bitewing radiology images using convolutional neural networks. The dataset utilized in this research includes 713 patient images obtained from the Samin Maxillofacial Radiology Center located in Tehran, Iran. The images were captured between June 2020 and January 2022 and underwent processing via four distinct Convolutional Neural Networks. The images were resized to 100 × 100 and then divided into two groups: 70% (4219) for training and 30% (1813) for testing. The four networks employed in this study were AlexNet, ResNet50, VGG16, and VGG19.
Results
Among different well-known CNN architectures compared in this study, the VGG19 model was found to be the most accurate, with a 93.93% accuracy.
Conclusion
This promising result indicates the potential for developing an automatic AI-based dental caries diagnostic model from Bitewing images. It has the potential to serve patients or dentists as a mobile app or cloud-based diagnosis service (clinical decision support system).
