Descriptive analysis of dental X-ray images using various practical methods: A review

Kumar, Anuj; Bhadauria, H.S.; Singh, Annapurna

doi:10.7717/peerj-cs.620

Cited by 35 publications

(26 citation statements)

References 133 publications

(313 reference statements)

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“…This paper presents a pipelined approach using AlexNet model. The reason for selection of AlexNet is that AlexNet has been known to excel at image classification tasks(Kumar et al, 2021 ), particularly when working with large real-world data sets such as the one used in this study. This is because it is capable of extracting both deep and baseline visual features.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Periapical dental X-ray image classification using deep neural networks

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Periapical dental X-ray image classification using deep neural networks

et al. 2022

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“…It considers three important characteristics for analysing dental CBCT with noisy labels and limited labelled sample size, and availability of oral medicine knowledge. A comprehensive survey of dental image segmentation and analysis is provided by investigating more than 130 research works conducted through various dental imaging modalities, such as various modes of X‐ray, CT (computed tomography), and CBCT (cone beam computed tomography) [36]. An AI driven tool that combined two deep convolutional neural networks with expert refinement is proposed by ref.…”

Section: Related Workmentioning

confidence: 99%

The local ternary pattern encoder–decoder neural network for dental image segmentation

Salih

Duffy

2022

IET Image Processing

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Recent advances in medical imaging analyses, especially the use of deep learning, are helping to identify, detect, classify, and quantify patterns in radiographs. At the centre of these advances is the ability to explore hierarchical feature representations learned from data. Deep learning is invaluably becoming the most sought out technique, leading to enhanced performances in the analysis of medical applications and systems. Deep learning techniques have achieved improved performance results in dental image segmentation. Segmentation of dental radiographs is a crucial step that helps dentists to diagnose dental caries. However, the performance of the deep networks used for these analyses are restrained by various challenging features found in dental carious lesions. Segmentation of dental images is often difficult due to the vast variety of types of topology, intricacies of medical structure and poor image quality caused by conditions such as low contrast, noise, irregular, and fuzzy border edges. These issues are exacerbated by low numbers of data images available for any particular analysis. A robust local ternary pattern encoder-decoder network (LTPEDN) is proposed to overcome dental image segmentation challenges and minimise the computational resources required. This new architecture is a modification of existing methods using an LTP. Images are preprocessed via augmentation and normalisation techniques to increase and prepare the datasets. Thereafter, the dataset input is sent to the LTPEDN for training and testing the model. Segmentation is performed using the nonlearnable layers (the LTP layers) and the learnable layers (standard convolution layers), to extract the ROI of the teeth. The method was evaluated on an augmented dataset of 11, 000 dental images. It was trained on 8, 800 training set images and tested on 2, 200 testing set images. The new method is shown to be 94.32% accurate.

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“…Pembelajaran mendalam dan teknik kecerdasan buatan sangat berhasil dalam mengatasi dilema segmentasi yang menantang yang disajikan dalam berbagai penelitian [14][15][16][17][18], sehingga kami dapat meramalkan angin puyuh penemuan dan garis temuan di tahun-tahun mendatang, berdasarkan pencapaian yang merekomendasikan model pembelajaran mesin mengenai segmentasi semiotik untuk DXRI. Dalam survei yang ada [19,20], berbagai teknik dan metode telah dibahas untuk DXRI. Dalam [21] teknik segmentasi dibagi menjadi tiga kelas: berbasis piksel, berbasis tepi, dan berbasis luar area, dan selanjutnya diklasifikasikan menjadi pendekatan thresholding, clustering boundary-based, region-based, atau watershed.…”

Section: Pendahuluanunclassified

Pengembangan Ekstraksi dalam Identifikasi Kelainan Gigi pada Citra Dental X-ray imaging (DXRI)

Sumijan

2022

komtekinfo

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Many of us as Muslims do not know how to act when the call to prayer is heard, which turns out to have a very noble virtue. This animation is expected to provide insight for all of us about the virtues of answering the call to prayer. In addition, this animation can also attract and entertain the audience. Technological advances that are growing rapidly today cover all fields. One of them is the field of multimedia such as animation. This study aims to create 3D animation that is used as a learning medium. The application has the theme of etiquette when calling the call to prayer. 3D animation is the process of placing objects and characters in three-dimensional space and manipulating them to create the illusion of motion. Design in application development using Blender software and applications in making animation. The results of the resulting application present an interactive form of teaching for users in providing knowledge about manners when the call to prayer is heard. With this application, it is hoped that it can provide an alternative to provide a form of teaching, especially for children to explain etiquette when calling the call to prayer

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Descriptive analysis of dental X-ray images using various practical methods: A review

Cited by 35 publications

References 133 publications

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

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

The local ternary pattern encoder–decoder neural network for dental image segmentation

Pengembangan Ekstraksi dalam Identifikasi Kelainan Gigi pada Citra Dental X-ray imaging (DXRI)

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