Oral Diseases Recognition Based on Photographic Images

Mazin Mohammed, Salah Zrigui, Salah Zrigui, Mounir Zrigui

2024

Abstract

Recently, the automation diagnosis process of dental caries plays a critical role in medical applications. This paper presents a new dataset of photo-graphic images for six different types of oral diseases. The dataset is gathered and labelled by professional medical operators in the dentistry field. We use the collected dataset to train a binary classifier to determine whether the region of interests (ROI) needs detection or not inside the input image. Then, we train a detector to detect and localize the required ROI. Finally, we use the detected regions to train a CNN network by adopting transfer learning technique to classify various kinds of teeth diseases. With this model, we obtained an almost 93 % accuracy by modifying and re-training the pre-trained model VGG19.

Paper Citation

in Harvard Style

Mohammed M., Zrigui S. and Zrigui M. (2024). Oral Diseases Recognition Based on Photographic Images. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 486-493. DOI: 10.5220/0012361500003636

in Bibtex Style

@conference{icaart24,
author={Mazin Mohammed and Salah Zrigui and Mounir Zrigui},
title={Oral Diseases Recognition Based on Photographic Images},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={486-493},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012361500003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Oral Diseases Recognition Based on Photographic Images
SN - 978-989-758-680-4
AU - Mohammed M.
AU - Zrigui S.
AU - Zrigui M.
PY - 2024
SP - 486
EP - 493
DO - 10.5220/0012361500003636
PB - SciTePress