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

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

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

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