Abstract
Oral squamous cell carcinoma (OSCC) remains a major death causing oral cancer in developing countries. In recent years, tremendous development in medical imaging devices made microscopic colour images of biopsy samples available to the researchers. Image processing and machine learning techniques can be used to develop automatic cancer grading mechanism. In this work, automatic OSCC classifier using Linear Discriminant Analysis combined with Random Subspace is developed and analyzed. The proposed classifiers automatically classifies the input image in one of the four categories, namely: Normal, Grade-I, II or III. Total 83 colour and texture features are computed from the 100 Haemotoxylin and Eosin (H&E) stained images of oral mucosa. The overall accuracy of the proposed classifier is 93.5% with sensitivity and specificity of 0.89 and 0.95 respectively.
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References
Gupta, B., Johnson, N.W.: Oral cancer: Indian pandemic. Br. Dent. J. 222, p. 497 (2017)
Kerr, A.R., Shah, S.S.: Standard examination and adjunctive techniques for detection of oral premalignant and malignant lesions. J. Calif. Dent. Assoc. 41(329–31), 334–342 (2013)
Koyfman, S.A., Ismaila, N., Crook, D., et al.: Management of the neck in squamous cell carcinoma of the oral cavity and oropharynx: ASCO clinical practice guideline. J. Clin. Oncol. 37, 1753–1774 (2019). https://doi.org/10.1200/JCO.18.01921
Lee, S.L., Cabanero, M., Hyrcza, M., et al.: Computer-assisted image analysis of the tumor microenvironment on an oral tongue squamous cell carcinoma tissue microarray. Clin. Transl. Radiat. Oncol. 17, 32–39 (2019). https://doi.org/10.1016/J.CTRO.2019.05.001
Abram, T.J., Floriano, P.N., James, R., et al.: Development of a cytology-based multivariate analytical risk index for oral cancer. Oral Oncol. 92, 6–11 (2019). https://doi.org/10.1016/J.ORALONCOLOGY.2019.02.011
Ariji, Y., Fukuda, M., Kise, Y., et al.: Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 127, 458–463 (2019). https://doi.org/10.1016/J.OOOO.2018.10.002
Eid, R.A., Landini, G.: Quantification of the global and local complexity of the epithelial-connective tissue interface of normal, dysplastic, and neoplastic oral mucosae using digital imaging. Pathol. – Res. Pract. 199, 475–482 (2003). https://doi.org/10.1078/0344-0338-00448
Muthu Rama Krishnan, M., Shah, P., Choudhary, A., et al.: Textural characterization of histopathological images for oral sub-mucous fibrosis detection. Tissue Cell (2011). https://doi.org/10.1016/j.tice.2011.06.005
Eid, R.A.A., Landini, G.: Oral epithelial dysplasia: can quantifiable morphological features help in the grading dilemma? In: Proceedings of the 1st ImageJ User and Developer Conference (2006)
Akhter, M., Rahman, Q., Hossain, S., Molla, M.: A study on histological grading of oral squamous cell carcinoma and its co-relationship with regional metastasis. J. Oral Maxillofac. Pathol. (2011). https://doi.org/10.4103/0973-029X.84485
Olympus CX31 Binocular Microscope - Four Objectives - Reconditioned - New York Microscope Co. https://www.microscopeinternational.com/product/olympus-cx31-binocular-microscope-four-objectives. Accessed 3 Dec 2018
ITU: ITU standard 709
Loesdau, M., Chabrier, S., Gabillon, A.: Hue and Saturation in the RGB Color Space, pp. 203–212. Springer, Cham (2014)
Hall-Beyer, M.: GLCM Texture: A Tutorial v. 3.0 March 2017. Arts Res. Publ. (2017) https://doi.org/10.11575/PRISM/33280
Kumar, R., Srivastava, R., Srivastava, S.: Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features. J. Med. Eng. 2015, 1–14 (2015). https://doi.org/10.1155/2015/457906
Laws, K.I.: Rapid texture identification. In: 24th Annual Technical Symposium (1980)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7(2), 179—188 (1936)
Rao, C.R.: The Utilization of Multiple Measurements in Problems of Biological Classification. J. R. Stat. Soc. Ser. B 10, 159–203 (1948)
Reitsma, J.B., Glas, A.S., Rutjes, A.W.S., et al.: Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J. Clin. Epidemiol. (2005). https://doi.org/10.1016/j.jclinepi.2005.02.022
Melo, F.: Area under the ROC Curve. Encyclopedia of Systems Biology, pp. 38–39. Springer, New York, New York, NY (2013)
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Nawandhar, A., Kumar, N., Yamujala, L. (2020). Random Subspace Combined LDA Based Machine Learning Model for OSCC Classifier. In: Saha, S., Nagaraj, N., Tripathi, S. (eds) Modeling, Machine Learning and Astronomy. MMLA 2019. Communications in Computer and Information Science, vol 1290. Springer, Singapore. https://doi.org/10.1007/978-981-33-6463-9_3
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DOI: https://doi.org/10.1007/978-981-33-6463-9_3
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