Age Group Classification From Dental Panoramic Radiographs Using Deep Learning Techniques

Limpapat Bussaban · Ekkarat Boonchieng · Wannakamon Panyarak · Arnon Charuakkra · Pornpattra Chulamanee

7 citations0/0 files storedIEEE Access

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Forensic Anthropology and Bioarchaeology Studies Dental Radiography and Imaging Face recognition and analysis

Abstract

Deep learning approach has been utilized in dental age determination from dental radiographs. In this study, several deep learning models were employed to predict age from a dataset of dental panoramic radiographs (OPGs). The dataset, collected from the Oral and Maxillofacial Radiology Clinics within the Faculty of Dentistry, Chiang Mai University, comprised 785 subjects with each subject having one OPG image ranging from 20 to 85 years old. We divided our dataset into four experimental cases: 2, 4, 6, and 12 classes. We utilized four deep learning models—SqueezeNet, VGG19, ResNet152, and EfficientNet—for training. In the experimental results, the efficiency of each model was compared in terms of accuracy. We found that VGG19 achieved the highest average degrees of accuracy at 92.95%, 80%, and 75.66% in the two-class, four-class, and six-class cases, respectively, while EfficientNet attained the highest degree of accuracy at 69.11% in the twelve-class case.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 7 citations exist upstream.

References

2006Age estimation 2011Dental radiographic indicators, a key to age estimation 2020Deep Neural Networks for Chronological Age Estimation From OPG Images 2021Automated estimation of chronological age from panoramic dental X-ray images using deep learning 2011Forensic odontology, part 1. Dental identification 2022Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters 2023Dental Age Estimation Using Deep Learning: A Comparative Survey 2020Personal identification with orthopantomography using simple convolutional neural networks: a preliminary study

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.