Smoker Melanosis Classification Using Oral Photographic Feature Extraction Based On K-Nearest Neighbor
DOI:
https://doi.org/10.21512/ijcshai.v2i1.12418Keywords:
KNN, Gingiva, Pigmentation, Image Processing, Smoker MelanosisAbstract
Smoking is one of the causes of various diseases in the body. Smoking can also cause abnormal conditions that are pathological and physiological in the oral cavity, one of which is smoker melanosis. The clinical picture of pigmentation smoker melanosis is the presence of scattered brown spots with a diameter of less than 1 cm and is most often located on the gingiva. The data was taken using the oral photograph image capture method using a 12MP resolution camera, provided that the object distance from the camera was 6 cm and the flash was on. This analysis utilized the Gingiva Pigmentation Index (GPI) classification system proposed by Hedin, which compares the pigmented area, and Dummett's Oral Colour Index (DOPI), which assesses the density of pigmentation. In this study, the classification process was carried out with the KNN algorithm using features from digital image processing in the segmentation area, the average value of the red, green, and blue colour levels. The classification process uses the nearest neighbour value of 3 and a p-value of 2 to measure the distance to the nearest neighbor using the Minkowski distance formula. The results of the test data accuracy (1.0) with F1 scores for each class for test data DOPI 0 = 1.0, DOPI 1 = 1.0, DOPI 2 = 1.0, DOPI 3 = 1.0. Meanwhile, the classification process can use more up-to-date methods, such as CNN to improve classification accuracy.
References
A. Drovandi, P. A. Teague, B. Glass, and B. Malau-Aduli, “A systematic review of smoker and non-smoker perceptions of visually unappealing cigarette sticks,” Tob. Induc. Dis., vol. 16, no. January, pp. 1–11, 2018, doi: 10.18332/tid/82191.
S. M. Noar, D. B. Francis, C. Bridges, J. M. Sontag, K. M. Ribisl, and N. T. Brewer, “The impact of strengthening cigarette pack warnings: Systematic review of longitudinal observational studies,” Soc. Sci. Med., vol. 164, pp. 118–129, 2016, doi: 10.1016/j.socscimed.2016.06.011.
S. M. Noar, M. G. Hall, D. B. Francis, K. M. Ribis, J. K. Pepper, and N. T. Brewer, “Pictorial cigarette pack warnings: A meta-analysis of experimental studies,” Tob. Control, vol. 25, no. 3, pp. 341–354, 2016, doi: 10.1136/tobaccocontrol-2014-051978.
S. Vellappally, Z. Fiala, J. Smejkalová, V. Jacob, and R. Somanathan, “Smoking related systemic and oral diseases,” Acta Medica (Hradec Kralove), vol. 50, no. 3, pp. 161–166, 2007, doi: 10.14712/18059694.2017.76.
S. Araki, K. Murata, K. Ushio, and R. Sakai, “Dose-response relationship between tobacco consumption and melanin pigmentation in the attached gingiva,” Arch. Environ. Health, vol. 38, no. 6, pp. 375–378, 1983, doi: 10.1080/00039896.1983.10545823.
C. A. Hedin, “Smokers’ Melanosis: Occurrence and Localization in the Attached Gingiva,” Arch. Dermatol., vol. 113, no. 11, pp. 1533–1538, 1977, doi: 10.1001/archderm.1977.01640110053007.
T. Kato, S. Mizutani, H. Takiuchi, S. Sugiyama, T. Hanioka, and T. Naito, “Gingiva pigmentation affected by smoking among different age groups: A quantitative analysis of gingiva pigmentation using clinical oral photographs,” Int. J. Environ. Res. Public Health, vol. 14, no. 8, Aug. 2017, doi: 10.3390/ijerph14080880.
M. A. Peres et al., “Oral diseases: a global public health challenge,” Lancet, vol. 394, no. 10194, pp. 249–260, 2019, doi: 10.1016/S0140-6736(19)31146-8.
M. Greenberg and M. Glick, Oral Medicine Diagnosis & Treatment, 2003.
A. Nuraini, A. Harmoko, and B. Kiswanjaya, “Improvement of smoker prediction system based on hyperspectral image with hybrid deep learning model,” in Int. Electron. Symp. 2021 Wireless Technol. Intell. Syst. Better Hum. Lives, IES 2021 - Proc., Sep. 2021, pp. 417–422, doi: 10.1109/IES53407.2021.9594025.
F. I. Hawari et al., “Respiratory health and quality of life in young exclusive, habitual smokers—a comparison of waterpipe smokers, cigarette smokers and non-smokers,” Int. J. COPD, vol. 14, pp. 1813–1824, 2019, doi: 10.2147/COPD.S205050.
K. R. Rosalin Hongsathavij and Y. Kuphasuk, “Correlation of environmental tobacco smoke to gingiva pigmentation and salivary alpha amylase in young adults,” Eur. J. Dent., vol. 11, no. 4, pp. 192–195, 2017, doi: 10.4103/ejd.ejd.
R. N. Smith, D. L. Lath, A. Rawlinson, M. Karmo, and A. H. Brook, “Gingiva inflammation assessment by image analysis: measurement and validation,” Int. J. Dent. Hyg., vol. 6, no. 2, pp. 137–142, 2008, doi: 10.1111/j.1601-5037.2008.00294.x.
H. Seshan and M. Shwetha, “Gingiva inflammation assessment: Image analysis,” J. Indian Soc. Periodontol., vol. 16, no. 2, pp. 231–234, 2012, doi: 10.4103/0972-124X.99267.
T. Kato et al., “Measurement of reduced gingiva melanosis after smoking cessation: A novel analysis of gingiva pigmentation using clinical oral photographs,” Int. J. Environ. Res. Public Health, vol. 13, no. 6, 2016, doi: 10.3390/ijerph13060598.
H. Kaur, S. Jain, and R. Sharma, “Duration of reappearance of gingiva melanin pigmentation after surgical removal—A clinical study,” J. Indian Soc. Periodontol., vol. 14, no. 2, p. 101, 2010, doi: 10.4103/0972-124X.70828.
A. Vyas, S. Yu, and J. Paik, Fundamentals of Digital Image Processing, 2018.
S. Shawal, M. Shoyab, and S. Begum, “Fundamentals of Digital Image Processing and Basic Concept of Classification,” Int. J. Chem. Process Eng. Res., vol. 1, no. 6, pp. 98–108, 2014, doi: 10.18488/journal.65/2014.1.6/65.6.98.108.
T. Eckhard, E. M. Valero, J. L. Nieves, and F. Mesa, “The Labial Teeth and Gingiva Photographic Image Database LTG-IDB,” [online resource].
Q. Wang and Y. Yuan, “Learning to resize image,” Neurocomputing, vol. 131, pp. 357–367, 2014, doi: 10.1016/j.neucom.2013.10.007.
G. Iwasokun, "Image Enhancement Methods: A Review," British Journal of Mathematics & Computer Science, vol. 4, no. 16, pp. 2251–2277, 2014, doi: 10.9734/bjmcs/2014/10332.
C. Saravanan, "Color image to grayscale image conversion," in 2010 2nd International Conference on Computer Engineering and Applications (ICCEA), 2010, vol. 2, pp. 196–199, doi: 10.1109/ICCEA.2010.192.
M. Nasir et al., "New method using image analysis to measure gingiva color," Journal of Osaka Dental University, vol. 38, no. 1, pp. 37–40, 2004, doi: 10.18905/jodu.38.1_37.
G. Gupta, "Algorithm for Image Processing Using Improved Median Filter and Comparison of Mean, Median and Improved Median Filter," International Journal of Soft Computing, no. 5, pp. 304–311, 2011. [Online]. Available: http://ijsce.org/attachments/File/Vol-1_Issue-5/E0234101511.pdf
P. Roy, S. Dutta, N. Dey, G. Dey, S. Chakraborty, and R. Ray, "Adaptive thresholding: A comparative study," in 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014, pp. 1182–1186, doi: 10.1109/ICCICCT.2014.6993140.
S. Dey, R. Sarkar, K. Chatterjee, P. Datta, A. Barui, and S. P. Maity, "Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis," Tissue & Cell, vol. 49, no. 2, pp. 296–306, 2017, doi: 10.1016/j.tice.2017.01.009.
A. Rana, G. Yauney, L. C. Wong, O. Gupta, A. Muftu, and P. Shah, "Automated segmentation of gingiva diseases from oral images," in 2017 IEEE Healthcare Innovation Point-of-Care Technologies (HI-POCT), 2017, vol. 2017-Dec., pp. 144–147, doi: 10.1109/HIC.2017.8227605.
T. Eckhard, E. M. Valero, and J. L. Nieves, "Labial teeth and gingiva color image segmentation for gingiva health-state assessment," in 6th European Conference on Colour in Graphics, Imaging, and Vision (CGIV), 2012, pp. 102–107.
C. Nitesh V, B. Kevin W, H. Lawrence O, and P. K. W, "SMOTE: Synthetic Minority Over-sampling Technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002, doi: 10.1002/eap.2043.
G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, "KNN model-based approach in classification," in Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2888, pp. 986–996, 2003, doi: 10.1007/978-3-540-39964-3_62
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Computer Science and Humanitarian AI
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
