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
Publication Title
International Journal of Imaging Systems and Technology
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