Proceedings of the 8th International Conference on Education, Management, Information and Management Society (EMIM 2018)

Gingivitis Identification via Grey-level Cooccurrence Matrix and Extreme Learning Machine

Authors
Wen Li, Yiyang Chen, Leiying Miao, Mackenzie Brown, Weibin Sun, Xuan Zhang
Corresponding Author
Wen Li
Available Online August 2018.
DOI
https://doi.org/10.2991/emim-18.2018.98How to use a DOI?
Keywords
Gingivitis; Graylevel Cooccurrence Matrix; Extreme Learning Machine
Abstract
The diagnosis of gingivitis often occurs years later by using a series of conventional oral examination, and they depended a lot on dental records which are physically and mentally laborious task for dentists. In this study, our research presented a new method to diagnose gingivitis, which is based on gray-level cooccurrence matrix (GLCM) and extreme learning machine (ELM). The experiments demonstrate that this method is more accurate and sensitive than two state-of-the-art approaches: naïve Bayes classifier and wavelet energy.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Wen Li
AU  - Yiyang Chen
AU  - Leiying Miao
AU  - Mackenzie Brown
AU  - Weibin Sun
AU  - Xuan Zhang
PY  - 2018/08
DA  - 2018/08
TI  - Gingivitis Identification via Grey-level Cooccurrence Matrix and Extreme Learning Machine
BT  - 8th International Conference on Education, Management, Information and Management Society (EMIM 2018)
PB  - Atlantis Press
SN  - 2352-5398
UR  - https://doi.org/10.2991/emim-18.2018.98
DO  - https://doi.org/10.2991/emim-18.2018.98
ID  - Li2018/08
ER  -