Deep Learning for Caries Detection using Optical Coherence Tomography

Huang, Yafang; S, Lee

doi:10.1101/2021.05.04.21256502

Cited by 9 publications

(9 citation statements)

References 26 publications

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“…In experimental studies carried out based on different CNN architectures, 599 images were used for training and the rest for testing. As a result, they achieved a 95.21% success rate with the ResNet-152 architecture [19]. Bui et al proposed a hybrid model for the detection of caries based on support vector machine (SVM) classifier using deep and geometric features.…”

Section: Literature Reviewmentioning

confidence: 99%

Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network

et al. 2022

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Panoramic and periapical radiograph tools help dentists in diagnosing the most common dental diseases, such as dental caries. Generally, dental caries is manually diagnosed by dentists based on panoramic and periapical images. For several reasons, such as carelessness caused by heavy workload and inexperience, manual diagnosis may cause unnoticeable dental caries. Thus, computer-based intelligent vision systems supported by machine learning and image processing techniques are needed to prevent these negativities. This study proposed a novel approach for the automatic diagnosis of dental caries based on periapical images. The proposed procedure used a multi-input deep convolutional neural network ensemble (MI-DCNNE) model. Specifically, a score-based ensemble scheme was employed to increase the achievement of the proposed MI-DCNNE method. The inputs to the proposed approach were both raw periapical images and an enhanced form of it. The score fusion was carried out in the Softmax layer of the proposed multi-input CNN architecture. In the experimental works, a periapical image dataset (340 images) covering both caries and non-caries images were used for the performance evaluation of the proposed method. According to the results, it was seen that the proposed model is quite successful in the diagnosis of dental caries. The reported accuracy score is 99.13%. This result shows that the proposed MI-DCNNE model can effectively contribute to the classification of dental caries.

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Section: Literature Reviewmentioning

confidence: 99%

Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network

et al. 2022

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“…Deep learning has recently emerged as a new method for image-based caries detection. The detection mainly focused on full image classification or grading using various convolutional neural network architectures [ 106 – 110 ]. Specifically, Huang et al tested AlexNet, VGG-16, ResNet-152, Xception and ResNext-101 to classify OCT images into no caries, superficial demineralization and dentine caries [ 110 ].…”

Section: The Role Of Oct In Assessing Tooth Demineralizationmentioning

confidence: 99%

“…The detection mainly focused on full image classification or grading using various convolutional neural network architectures [ 106 – 110 ]. Specifically, Huang et al tested AlexNet, VGG-16, ResNet-152, Xception and ResNext-101 to classify OCT images into no caries, superficial demineralization and dentine caries [ 110 ]. They achieved the best results on ResNet-152 with accuracy of 95.21% and sensitivity of 98.85%.…”

Section: The Role Of Oct In Assessing Tooth Demineralizationmentioning

confidence: 99%

Assessment of demineralized tooth lesions using optical coherence tomography and other state-of-the-art technologies: a review

et al. 2022

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Tooth demineralization is one of the most common intraoral diseases, encompassing (1) caries caused by acid-producing bacteria and (2) erosion induced by acid of non-bacterial origin from intrinsic sources (e.g. stomach acid reflux) and extrinsic sources (e.g. carbonated drinks). Current clinical assessment based on visual-tactile examination and standardized scoring systems is insufficient for early detection. A combination of clinical examination and technology is therefore increasingly adapted. This paper reviews various procedures and technologies that have been invented to diagnose and assess the severity of tooth demineralization, with focus on optical coherence tomography (OCT). As a micron-resolution non-invasive 3D imaging modality, variants of OCT are now available, offering many advantages under different working principles for detailed analytical assessment of tooth demineralization. The roles, capabilities and impact of OCT against other state-of-the-art technologies in both clinical and research settings are described. (139 words).

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“…In comparison to AlexNet, VGG, Xception, and ResNeXt, the ResNet architecture has a high level of accuracy for dental caries detection [16]. By utilizing earlier layer activations, ResNet (residual network) addresses the primary issue of fading gradients that networks encounter when using shortcut connections.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Convolutional Neural Network for Automatic Caries Detection in Digital Radiograph Panoramic on Small Dataset

Fariza

Asmara

Rojaby

et al. 2022

2022 International Conference on Data and Software Engineering (ICoDSE)

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Dental caries or tooth decay is damage to the hard tissues of the teeth that can occur in the enamel, dentin, and cementum areas. Panoramic radiography is a screening tool for tactile or visual examination of the oral cavity which is useful for further diagnosis and treatment. The process of segmentation of panoramic radiographs is a difficult process because there is no homogeneity between panoramic images with one another. Noise levels, vertebral column images, and low contrast are the main challenges in image processing. This study evaluates CNN to detect caries automatically on panoramic radiographs on a small dataset. The dataset consisted of manually cropped maxillary and mandibular premolars and molars. An augmentation strategy consisting of horizontal flip, vertical flip, and affine transformation is used to produce a wider variety of images. This study compares the architecture of non-pretrained and pretrained models consisting of 3-layer CNN, 3-layer CNN with batch normalization, ResNet18, and ResNeXt50 32×4d. Evaluation was carried out on 400 training data and 76 testing data. Combination of augmentation strategies and pre-trained ResNet18 and ResNeXt50 32×4d achieves high accuracy compared to other models.

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Deep Learning for Caries Detection using Optical Coherence Tomography

Cited by 9 publications

References 26 publications

Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network

Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network

Assessment of demineralized tooth lesions using optical coherence tomography and other state-of-the-art technologies: a review

Evaluation of Convolutional Neural Network for Automatic Caries Detection in Digital Radiograph Panoramic on Small Dataset

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