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Expression of Concern: A gingivitis identification method based on contrast‐limited adaptive histogram equalization, gray‐level co‐occurrence matrix, and extreme learning machine
Author(s) -
Li Wen,
Chen Yiyang,
Sun Weibin,
Brown Mackenzie,
Zhang Xuan,
Wang Shuihua,
Miao Leiying
Publication year - 2019
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22298
Subject(s) - adaptive histogram equalization , histogram equalization , gray level , artificial intelligence , contrast (vision) , extreme learning machine , gingivitis , pattern recognition (psychology) , histogram , computer science , dentistry , medicine , image (mathematics) , artificial neural network
The diagnosis of gingivitis often occurs years later 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 contrast‐limited adaptive histogram equalization (CLAHE), gray‐level co‐occurrence matrix (GLCM), and extreme learning machine (ELM). Our dataset contains 93 images: 58 gingivitis images and 35 healthy control images. The experiments demonstrate that the average sensitivity, specificity, precision, and accuracy of our method is 75%, 73%, 74% and 74%, respectively. This method is more accurate and sensitive than three state‐of‐the‐art approaches.