Automatic Tooth Detection and Numbering Using a Combination of a CNN and Heuristic Algorithm

Kim, Changgyun; Kim, Donghyun; Jeong, Ho-Gul; Yoon, Suk-Ja; Youm, Sekyoung

doi:10.3390/app10165624

Cited by 47 publications

(40 citation statements)

References 20 publications

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“…By convolution calculation, the adjacent pixel is multiplied by the convolution filter [ 7 ]. In practice, deep learning systems have been used in detecting teeth problems using CNN-based methods [ 8 ]. These CNN-based methods combine region-based convolution neural network (R-CNN), single-shot multi-box detector, and heuristic methods for detecting teeth, implants, and crowns.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Mandibular Fractures in Panoramic Radiographs Using Deep Learning

et al. 2021

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Mandibular fracture is one of the most frequent injuries in oral and maxillo-facial surgery. Radiologists diagnose mandibular fractures using panoramic radiography and cone-beam computed tomography (CBCT). Panoramic radiography is a conventional imaging modality, which is less complicated than CBCT. This paper proposes the diagnosis method of mandibular fractures in a panoramic radiograph based on a deep learning system without the intervention of radiologists. The deep learning system used has a one-stage detection called you only look once (YOLO). To improve detection accuracy, panoramic radiographs as input images are augmented using gamma modulation, multi-bounding boxes, single-scale luminance adaptation transform, and multi-scale luminance adaptation transform methods. Our results showed better detection performance than the conventional method using YOLO-based deep learning. Hence, it will be helpful for radiologists to double-check the diagnosis of mandibular fractures.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection of Mandibular Fractures in Panoramic Radiographs Using Deep Learning

et al. 2021

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“…The images are split into four quarters and fed to the AlexNet for tooth classification. Kim [41] presented automatic tooth detection and classification methods that combine the regional convolutional neural network (RCNN), Single Shot Detector, and heuristic methods. The proposed algorithm yielded precision values of 84.5%, sensitivity values of 75.5%, and specificity values of 80.4%.…”

Section: Related Workmentioning

confidence: 99%

Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems

Panetta

Rajendran

Ramesh

et al. 2022

IEEE J. Biomed. Health Inform.

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The application of Artificial Intelligence in dental healthcare has a very promising role due to the abundance of imagery and non-imagery-based clinical data. Expert analysis of dental radiographs can provide crucial information for clinical diagnosis and treatment. In recent years, Convolutional Neural Networks have achieved the highest accuracy in various benchmarks, including analyzing dental X-ray images to improve clinical care quality. The Tufts Dental Database, a new X-ray panoramic radiography image dataset, has been presented in this paper. This dataset consists of 1000 panoramic dental radiography images with expert labeling of abnormalities and teeth. The classification of radiography images was performed based on five different levels: anatomical location, peripheral characteristics, radiodensity, effects on the surrounding structure, and the abnormality category. This first-of-its-kind multimodal dataset also includes the radiologist's expertise captured in the form of eye-tracking and think-aloud protocol. The contributions of this work are 1) publicly available dataset that can help researchers to incorporate human expertise into AI and achieve more robust and accurate abnormality detection; 2) a benchmark performance analysis for various state-of-the-art systems for dental radiograph image enhancement and image segmentation using deep learning; 3) an in-depth review of various panoramic dental image datasets, along with segmentation and detection systems. The release of this dataset aims to propel the development of AI-powered automated abnormality detection and classification in dental panoramic radiographs, enhance tooth segmentation algorithms, and the ability to distill the radiologist's expertise into AI.

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“…The backbone architecture used in this project is "RESNET50" [17], [18]. A backbone architecture is a feature pyramid network-style deep neural network.…”

Section: A Mask Rcnnmentioning

confidence: 99%

Mask RCNN with RESNET50 for Dental Filling Detection

Aparna¹,

Muppavaram²,

Ramayanam³

et al. 2021

IJACSA

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Automatic Tooth Detection and Numbering Using a Combination of a CNN and Heuristic Algorithm

Cited by 47 publications

References 20 publications

Automatic Detection of Mandibular Fractures in Panoramic Radiographs Using Deep Learning

Automatic Detection of Mandibular Fractures in Panoramic Radiographs Using Deep Learning

Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems

Mask RCNN with RESNET50 for Dental Filling Detection

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