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Gingivitis identification via multichannel gray-level co-occurrence matrix and particle swarm optimization neural network

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posted on 26.03.2020, 15:41 by Wen Li, Xianwei Jiang, Weibin Sun, Yu-Dong Zhang, Shui-Hua Wang, Chao Liu, Xuan Zhang, Wei Zhou, Leiying Miao
The oral maintenance of patients with periodontal disease mainly depends on clinical examination. However, insufficient number of medical workers cannot carry out detailed oral health education for a large number of patients within limited time and provide these patients with proper and effective oral health nursing methods. In this research, our study put forward a new Artificial Intelligence (AI) based method to diagnose chronic gingivitis, which is based on multichannel gray-levelco-occurrence matrix (MGLCM) and particle swarm optimization neural network(PSONN). Meanwhile, different training algorithms were used as comparison groups. The data set contains 800 images: 400 chronic gingivitis images and 400 healthy gingiva images. The results certify that the specificity, sensitivity, precision, accuracy and F1 Score of MGLCM (PSONN as a classifier) method is 78.17%, 78.23%, 78.24%, 78.20%and 78.17%, respectively. The association of MGLCM and PSONN is more accurate and efficient than approaches: NBC, WN+SVM,ELM and CLAHE+ELM.

History

Citation

Int. J. Imaging Syst. Technol., 2019; 1–11.

Author affiliation

Department of Informatics

Version

AM (Accepted Manuscript)

Published in

International Journal of Imaging Systems and Technology

Publisher

WILEY

issn

0899-9457

eissn

1098-1098

Acceptance date

12/11/2019

Copyright date

2019

Available date

06/12/2019

Publisher version

https://onlinelibrary.wiley.com/doi/full/10.1002/ima.22385

Language

English