A gingivitis identification method based on contrast‐limited adaptive histogram equalization, gray‐level co‐occurrence matrix, and extreme learning machine. Issue 1 (17th November 2018)

• Record Type: Journal Article
• Authors: Li, Wen; Chen, Yiyang; Sun, Weibin; Brown, Mackenzie; Zhang, Xuan; Wang, Shuihua; Miao, Leiying
• Is Part Of: International journal of imaging systems and technology. Volume 29:Issue 1(2019)
• Page Start: 77
• Page End: 82
• Publication Date: 2018-11-17
• Subjects: contrast‐limited adaptive histogram equalization -- extreme learning machine -- gingivitis -- gray‐level co‐occurrence matrix; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22298
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: 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.