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

Abstract

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: naï ve Bayes classifier and wavelet energy.

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

“…Overall, the research was able to categorize molars, premolars, canines, and incisors with an accuracy of 79.75% [ 48 ]. Li et al [ 49 ] developed another automated technique for categorizing tooth kinds on dental pictures by utilizing ELM and a gray-level cooccurrence matrix (GLCM) [ 50 ]. Experiments indicate that the suggested technique is more sensitive and precise than the naïve Bayes and the wavelet energy.…”