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JACIII Vol.26 No.2 pp. 217-225
doi: 10.20965/jaciii.2022.p0217
(2022)

Paper:

Individual Differences in Cut-Out Areas of Oral Images in Oral Mucosal Disease Diagnosis Support System

Nanto Ozaki*, Taishi Ohtani**, Manabu Habu**, Kazuhiro Tominaga**, and Keiichi Horio***

*National Institute of Technology, Oshima College
1091-1 Oaza-Komatsu, Suo-Oshima-cho, Oshima-gun, Yamaguchi 742-2193, Japan

**Kyushu Dental University
2-6-1 Manazuru, Kokurakitaku, Kitakyushu, Fukuoka 803-8580, Japan

***School of Life Science and Systems Engineering, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 803-0135, Japan

Received:
February 28, 2018
Accepted:
February 7, 2022
Published:
March 20, 2022
Keywords:
diagnosis support system, oral mucosal disease, invidual difference, emsemble classification
Abstract

Oral mucosal disease is likely to cause various disorders after treatment to occur in a domain called the oral cavity. Therefore, we are developing a diagnostic support system for early screening of oral mucosal disease. There is a problem of individual differences in the cut-out of the disease area from the original intraoral image in system development. In this study, we analyzed the relationships between cutout areas, extracted features and classification rates and investigated the relationship between individual differences. Therefore, we focused on how to eliminate the subjects. Group classification was then performed and identification was performed using an oral mucosal diagnosis support system with ensemble learning. The experimental results revealed relationships between the excision range, identification rate, and feature value.

Cite this article as:
N. Ozaki, T. Ohtani, M. Habu, K. Tominaga, and K. Horio, “Individual Differences in Cut-Out Areas of Oral Images in Oral Mucosal Disease Diagnosis Support System,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.2, pp. 217-225, 2022.
Data files:
References
  1. [1] National Cancer Center Japan, “Cancer Statistics Forecast for 2017,” https://ganjoho.jp/reg_stat/statistics/stat/short_pred.html (in Japanese) [accessed February 20, 2018]
  2. [2] M. W. Finkelstein, “A Guide to Clinical Differential Diagnosis of Oral Mucosal Lesions,” dentalcare.com, 2010.
  3. [3] M. Radwan-Oczko and M. Mendak, “Differential diagnosis of oral leukoplakia and lichen planus – On the basis of literature and own observations,” J. Stoma., Vol.64, No.5-6, pp. 355-370, 2011.
  4. [4] S. E. Mofty, “Early detection of oral cancer, “J. Oral Maxillofac. Surg., Vol.1, pp. 25-31, 2010.
  5. [5] M. W. Lingen, J. R. Kalmar, T. Karrison, and P. M. Speight, “Critical evaluation of diagnostic aids for the detection of oral cancer,” Oral Oncology, Vol.44, Issue 1, pp. 10-22, 2007.
  6. [6] K. Doi, “Current status and future potential of computer-aided diagnosis in medical imaging,” The British J. of Radiology, Vol.78, No.supple 1, pp. s3-s19, 2014.
  7. [7] K. Anuradha and K. Sankaranarayanan, “Identification of Suspicious Regions to Detect Oral Cancers at An Earlier Stage – A Literature Survey,” Int. J. of Advances in Engineering ad Technology, Vol.3, Issue 1, pp. 84-91, 2012.
  8. [8] P. Wilder-Smith, J. Holtzman, J. Epstein, and A. Le, “Optical diagnosis in the oral cavity: An overview,” Oral Disease, Vol.16, pp. 717-728, 2010.
  9. [9] P. M. Lane, T. Gilhuly, P. D. Whitehead et al., “Simple device for the direct visualization of oral-cavity tissue fluorescence,” J. of Biomedical Optics, Vol.11, Issue 2, Article No.024006, 2006.
  10. [10] C. J. C. Burges, “A Tutorial on Support Vector Machine for Pattern Recognition,” Data Mining and Knowledge Discovery,” Vol.2, pp. 121-167, 1998.
  11. [11] S. Motoki, K. Saito, M. Habu, K. Horio, and K. Tominaga, “Effect of Image Resolution in Discrimination of Oral Mucosal Disease Based on Intraoral Images,” Proc. of Int. Workshop on Smart Info-Media Systems in Asia (SISA2014), pp. 46-49, 2014.
  12. [12] K. Horio, S. Motoki, K. Saito, M. Habu, and K. Tominaga,“Effect of manual image cutout in diagnosis support system of oral mucosal disease based on intraoral image,” 2015 15th Int. Symp. on Communications and Information Technologies (ISCIT), pp. 133-136, 2015.
  13. [13] Y. Nishi, K. Horio, K. Saito, M. Habu, and K. Tominaga, “Discrimination of Oral Mucosal Disease Inspired by Diagnostic Process of Specialist,” J. of Medical and Bioengineering, Vol.2, No.1, pp. 57-61, 2013.
  14. [14] I. W. Scopp, “Oral medicine A clinical approach with basic science correlation,” 2nd edition, C. V. Mosby Company, 1973.
  15. [15] N. K. Wood and P. W. Goaz, “Differetial Diagnosis of Oral Legions,” 3rd edition, C. V. Mosby Company, 1985.
  16. [16] N. Ozaki, K. Saito, T. Ohtani, M. Habu, K. Tominaga, and K. Horio, “Ensemble Classifier Which Is Robust Against Individual Difference in Diagnosis Support System of Oral Mucosal Disease,” Proc. of Int. Workshop on Smart Info-Media Systems in Asia, No.SS6-2, pp. 258-262, 2016.

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Last updated on Sep. 30, 2022