Gingivitis Identification via Grey-level Cooccurrence Matrix and Extreme Learning Machine
Wen Li, Yiyang Chen, Leiying Miao, Mackenzie Brown, Weibin Sun, Xuan Zhang
Available Online August 2018.
- https://doi.org/10.2991/emim-18.2018.98How to use a DOI?
- Gingivitis; Graylevel Cooccurrence Matrix; Extreme Learning Machine
- 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.
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 SP - 486 EP - 492 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 -