Title
A gingivitis identification method based on contrast-limited adaptive histogram equalization, gray-level co-occurrence matrix, and extreme learning machine
Document Type
Journal Article
Publisher
John Wiley and Sons Inc.
School
School of Engineering
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. © 2018 Wiley Periodicals, Inc.
DOI
10.1002/ima.22298
Access Rights
free_to_read
Comments
Originally published as: Li, W., Chen, Y., Sun, W., Brown, M., Zhang, X., Wang, S., & Miao, L. (2019). A gingivitis identification method based on contrast-limited adaptive histogram equalization, gray-level co-occurrence matrix, and extreme learning machine. International Journal of Imaging Systems and Technology, 29(1), 77-82. Original article available here