@inproceedings{ec290c276e9c469c8eb3851d138909bf,
title = "Toward development of automated grading system for carious lesions classification using deep learning and OCT imaging",
abstract = "Dental caries remains the most prevalent chronic disease in both children and adults. Optical coherence tomography (OCT) is a noninvasive optical imaging modality utilized to image oral samples to diagnose carious lesions, but detecting early stage dental caries with high-level accuracy remains challenging. Deep learning models have been employed to classify OCT images for various healthcare applications. In this paper, human tooth specimens were imaged ex vivo using OCT imaging systems, and a three-class grading system based on deep learning model for detection and classification of carious lesions was developed. This study is a step forward in the development of automated deep learning/OCT imaging system for early dental caries diagnosis.",
keywords = "carious lesions., classification, convolutional neural networks, deep learning, image processing, optical coherence tomography",
author = "Salehi, \{Hassan S.\} and Majd Barchini and Qingguang Chen and Mina Mahdian",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2581318",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Gimi, \{Barjor S.\} and Andrzej Krol",
booktitle = "Medical Imaging 2021",
}