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An Automatic Classification Methods in Oral Cancer Detection

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Health Informatics: A Computational Perspective in Healthcare

Part of the book series: Studies in Computational Intelligence ((SCI,volume 932))

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Abstract

Oral cancer is a bunch of many related diseases and it is significantly important to diagnose the infected region with the efficient detection methods. This book chapter represented automatic classification segmentation methods for the detection of oral cancer especially ensemble-based segmentation methods. The estimated outcomes have been discussed with essential parameters. A relative study of segmentation methods have also been provided for the advancement of detection of infected regions. In addition, some best elementary approaches are also discussed to increase the recovery score. The goal is to increase the survival rate by diagnosing the oral cancer in less time duration and with more efficient detection methods which will be a significant step in the area of medical imaging.

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References

  1. CRUK. https://www.cancerresearchuk.org 2017, Accessed on May 1, 2020.

  2. Shivam, B. (2020). Oral cancer (lips and tongue) images, Version 1, Accessed on Dec 09, 2020 from https://www.kaggle.com/shivam17299/oral-cancer-lips-and-tongue-images.

  3. Sabuncu, M. R., Yeo, B. T. T., Van Leemput, K., Fischl, B., & Golland, P. (2010). A generative model for image segmentation based on label fusion. IEEE Transactions on Medical Imaging, 29(10), 1714–1729.

    Article  Google Scholar 

  4. Pohl, K. M., Fisher, J., Grimson, W. E. L., Kikinis, R., & Wells, W. M. (2006). A Bayesian model for joint segmentation and registration. NeuroImage, 31(1), 228–239.

    Article  Google Scholar 

  5. Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839–851.

    Article  Google Scholar 

  6. Kumar, R., Khambete, N., & Priya, E. (2011). Extraoral periapical radiography: an alternative approach to intraoral periapical radiography. Imaging Science in Dentistry, 41(4), 161

    Google Scholar 

  7. Wang, C.-W., Huang, C.-T., Lee, J.-H., Li, C.-H., Chang, S.-W., Siao, M.-J., et al. (2016). A benchmark for comparison of dental radiography analysis algorithms. Medical Image Analysis, 31, 63–76.

    Article  Google Scholar 

  8. Silverman, S. (2003). Epidemiology. In Oral Cancer (pp. 1–6). American Cancer Society.

    Google Scholar 

  9. Morikawa, T., Kozakai, A., Kosugi, A., Bessho, H., & Shibahara, T. (2020). Image processing analysis of oral cancer, oral potentially malignant disorders, and other oral diseases using optical instruments. International Journal of Oral and Maxillofacial Surgery, 49(4), 515–521.

    Article  Google Scholar 

  10. Alarifi, A., & Alwadain, A. (2019). Computer-aided cancer classification system using a hybrid level-set image segmentation. Measurement, 148, 106864.

    Article  Google Scholar 

  11. Abdi, A. H., Kasaei, S., & Mehdizadeh, M. (2015). Automatic segmentation of mandible in panoramic x-ray. Journal of Medical Imaging., 2(4), 044003.

    Article  Google Scholar 

  12. Fahmy, G. (2005). Toward an automated dental identification system. Journal of Electronic Imaging, 14(4), 043018.

    Article  Google Scholar 

  13. Jain, A. K., & Chen, H. (2004). Matching of dental X-ray images for human identification. Pattern Recognition, 37(7), 1519–1532.

    Article  Google Scholar 

  14. Lin, P. L., Lai, Y. H., & Huang, P. W. (2010). An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information. Pattern Recognition, 43(4), 1380–1392.

    Article  Google Scholar 

  15. Lin, P.-L., Lai, Y.-H., & Huang, P.-W. (2012). Dental biometrics: Human identification based on teeth and dental works in bitewing radiographs. Pattern Recognition, 45(3), 934–946.

    Article  Google Scholar 

  16. Mahoor, M. H., & Abdel-Mottaleb, M. (2005). Classification and numbering of teeth in dental bitewing images. Pattern Recognition, 38(4), 577–586.

    Article  Google Scholar 

  17. Nassar, D. E. M., & Ammar, H. H. (2007). A neural network system for matching dental radiographs. Pattern Recognition, 40(1), 65–79.

    Article  MATH  Google Scholar 

  18. Nomir, O., & Abdel-Mottaleb, M. (2005). A system for human identification from X-ray dental radiographs. Pattern Recognition, 38(8), 1295–1305.

    Article  MATH  Google Scholar 

  19. Zhou, J., & Abdel-Mottaleb, M. (2005). A content-based system for human identification based on bitewing dental X-ray images. Pattern Recognition, 38(11), 2132–2142.

    Article  Google Scholar 

  20. Frejlichowski, D., & Czapiewski, P. (2013). An Application of the curvature scale space shape descriptor for forensic human identification based on orthopantomograms. In K. Saeed, R. Chaki, A. Cortesi, & S. Wierzchoń (Eds.), Computer information systems and industrial management (Vol. 8104, pp. 67–76)., CISIM 2013, Lecture Notes in Computer Science Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  21. Son, L. H., Tuan, T. M., Fujita, H., Dey, N., Ashour, A. S., & Ngoc, V. T. N. (2018). Dental diagnosis from X-Ray images: An expert system based on fuzzy computing. Biomedical Signal Processing and Control, 39, 64–73.

    Article  Google Scholar 

  22. Guillaume, S. (2001). Designing fuzzy inference systems from data: An interpretability-oriented review. IEEE Transactions on Fuzzy Systems, 9(3), 426–443.

    Article  Google Scholar 

  23. Priyatharshini, R., & Chitrakala, S. (2018). A self-learning fuzzy rule-based system for risk-level assessment of coronary heart disease. IETE Journal of Research, 65(3), 288–297.

    Article  Google Scholar 

  24. Chattopadhyay, S., Davis, R. M., Menezes, D. D., Singh, G., Acharya, R. U., & Tamura, T. (2010). Application of bayesian classifier for the diagnosis of dental pain. Journal of Medical Systems, 36(3), 1425–1439.

    Article  Google Scholar 

  25. Chattopadhyay, C., Kim, D. W., Gombos, D. S., Oba, J., Qin, Y., Williams, M. D., et al. (2016). Uveal melanoma: From diagnosis to treatment and the science in between. Cancer, 122(15), 2299–2312.

    Article  Google Scholar 

  26. Kavitha, M. S., Asano, A., Taguchi, A., Kurita, T., & Sanada, M. (2012). Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system. BMC Medical Imaging, 12(1).

    Google Scholar 

  27. Oad, K. K., DeZhi, X., & Butt, P. K. (2014). A fuzzy rule based approach to predict risk level of heart disease. Global Journal of Computer Science Technology, 14(3), 16–22.

    Google Scholar 

  28. Castillo, O., Melin, P., Ramírez, E., & Soria, J. (2012). Hybrid intelligent system for cardiac arrhythmia classification with Fuzzy K-Nearest Neighbors and neural networks combined with a fuzzy system. Expert Systems with Applications, 39(3), 2947–2955.

    Article  Google Scholar 

  29. Ramírez, J., Górriz, J. M., Salas-Gonzalez, D., Romero, A., López, M., Álvarez, I., et al. (2013). Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features. Information Sciences, 237, 59–72.

    Article  Google Scholar 

  30. Grabisch, M., Nguyen, H. T., & Walker, E. (1995). Introduction. In Fundamentals of uncertainty calculi with applications to fuzzy inference (pp. 1–3). Dordrecht Kluwer.

    Google Scholar 

  31. Doi, K. (2007). Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4–5), 198–211.

    Article  Google Scholar 

  32. Pretty, I. A., & Maupomé, G. (2014). A closer look at diagnosis in clinical dental practice: Part 1. Reliability, validity, specificity and sensitivity of diagnostic procedures. Journal (Canadian Dental Association), 70(4), 251–255.

    Google Scholar 

  33. Priyono, A., Ridwan, M., Alias, A. J., Rahmat, R. A., Hassan, A., Mohd. Ali, M. A. (2005) Generation of Fuzzy Rules with Subtractive Clustering. Jurnal Teknologi, 43(1), 143–153.

    Google Scholar 

  34. Son, L. H., & Thong, N. T. (2015). Intuitionistic fuzzy recommender systems: An effective tool for medical diagnosis. Knowledge-Based Systems, 74, 133–150.

    Article  Google Scholar 

  35. Thong, N. T., & Son, L. H. (2015). HIFCF: An effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis. Expert Systems with Applications, 42(7), 3682–3701.

    Article  Google Scholar 

  36. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203.

    Article  Google Scholar 

  37. Bouchachia, A., & Pedrycz, W. (2006). Data Clustering with partial supervision. Data Mining and Knowledge Discovery, 12(1), 47–78.

    Article  MathSciNet  MATH  Google Scholar 

  38. Bouchachia, A., & Vanaret, C. (2014). GT2FC: An online growing interval type-2 self-learning fuzzy classifier. IEEE Transactions on Fuzzy Systems, 22(4), 999–1018.

    Article  Google Scholar 

  39. Hamasuna, Y., & Endo, Y. (2012). On semi-supervised fuzzy c-means clustering for data with clusterwise tolerance by opposite criteria. Soft Computing, 17(1), 71–81.

    Article  Google Scholar 

  40. Yin, X., Shu, T., & Huang, Q. (2012). Semi-supervised fuzzy clustering with metric learning and entropy regularization. Knowledge-Based Systems, 35, 304–311.

    Article  Google Scholar 

  41. Portela, N. M., Cavalcanti, G. D. C., & Ren, T. I. (2014). Semi-supervised clustering for MR brain image segmentation. Expert Systems with Applications, 41(4), 1492–1497.

    Article  Google Scholar 

  42. Belkin, M., & Niyogi, P. (2002). Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems, 14, 585–591.

    Google Scholar 

  43. He, X., Cai, D., Wen, J.-R., Ma, W.-Y., & Zhang, H.-J. (2007). Clustering and searching WWW images using link and page layout analysis. ACM Transactions on Multimedia Computing, Communications, and Applications., 3(2), 10.

    Article  Google Scholar 

  44. Hu, Y., Yu, N., Li, Z., & Li, M. (2007) Image search result clustering and re-ranking via partial grouping. In 2007 IEEE International Conference on Multimedia and Expo (pp. 603–606).

    Google Scholar 

  45. Kavitha, N., Vijayarathna, S., Jothy, S. L., Oon, C. E., Chen, Y., Kanwar, J. R., et al. (2014). MicroRNAs: Biogenesis, roles for carcinogenesis and as potential biomarkers for cancer diagnosis and prognosis. Asian Pacific Journal of Cancer Prevention, 15(18), 7489–7497.

    Article  Google Scholar 

  46. Manh Tuan, T., & Hoang Son, L. (2016). A novel framework using graph-based clustering for dental X-ray image search in medical diagnosis. International Journal of Engineering and Technology., 8(6), 422–427.

    Article  Google Scholar 

  47. Valachovic, R. W., Douglass, C. W., Reiskin, A. B., Chauncey, H. H., & McNeil, B. J. (1986). The use of panoramic radiography in the evaluation of asymptomatic adult dental patients. Oral Surgery, Oral Medicine, Oral Pathology., 61(3), 289–296.

    Article  Google Scholar 

  48. Greenstein, G., & Cavallaro, J. (2014). Failed dental implants. The Journal of the American Dental Association, 145(8), 835–841.

    Article  Google Scholar 

  49. Narkhede, H. P. (2013). Review of image segmentation techniques. Journal of Image and Graphics., 1(8), 54–61.

    Google Scholar 

  50. Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037–2041.

    Article  MATH  Google Scholar 

  51. Lai Y. H., & Lin P. L. (2008) Effective segmentation for dental X-ray images using texture-based fuzzy inference system. In J. Blanc-Talon, S. Bourennane, W. Philips, D. Popescu, & P. Scheunders (Eds.), Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, Vol. 5259. Springer, Berlin, Heidelberg (2008)

    Google Scholar 

  52. Ghazali, K. H., Mustafa, M. M., Hussain, A., Bandar, M. E. C., Kuantan, G. (2007) Feature Extraction technique using SIFT keypoints descriptors. In International Conference on Electrical and Engineering and Informatics Institute Technology (pp. 17–19)

    Google Scholar 

  53. Tuan, T. M., Ngan, T. T., & Son, L. H. (2016). A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation. Applied Intelligence., 45(2), 402–428.

    Article  Google Scholar 

  54. Abdi, A., Kasaei, S. (2020) Panoramic dental X-rays with segmented mandibles. Mendeley Data, v2

    Google Scholar 

  55. Frejlichowski, D., & Wanat, R. (2011) Automatic segmentation of digital orthopantomograms for forensic human identification. In Image Analysis and Processing-ICIAP 2011 (pp. 294–302). Springer, Berlin, Heidelberg.

    Google Scholar 

  56. Shi, S.-W., Li, B., Dong, Y., Ge, Y., Qu, X., Lu, L.-G., et al. (2019). In vitroand clinical studies of gene therapy with recombinant human adenovirus-p53Injection for malignant melanoma. Human Gene Therapy Clinical Development., 30(1), 7–18.

    Article  Google Scholar 

  57. Panta, P (2019) Biopsy and oral squamous cell carcinoma histopathology. In Oral Cancer Detection. Novel Strategies and Clinical Impact (p. 142)

    Google Scholar 

  58. Muthulakshmi, M., & Kavitha, G. (2018) Analysis of myocardial ischemia from cardiac magnetic resonance images using adaptive fuzzy-based multiphase level set. In Computational Signal Processing and Analysis (pp. 11–22). Springer, Singapore

    Google Scholar 

  59. Shield, K. D., Ferlay, J., Jemal, A., Sankaranarayanan, R., Chaturvedi, A. K., Bray, F., et al. (2016) The global incidence of lip, oral cavity, and pharyngeal cancers by subsite in 2012. CA: A Cancer Journal for Clinicians, 67(1), 51–64

    Google Scholar 

  60. Subramanian, S. (2009). Cost-effectiveness of oral cancer screening: results from a cluster randomized controlled trial in India. Bulletin of the World Health Organization, 87(3), 200–206.

    Article  Google Scholar 

  61. Warnakulasuriya, S. (2009). Global epidemiology of oral and oropharyngeal cancer. Oral Oncology, 45(4–5), 309–316.

    Article  Google Scholar 

  62. Lingen, M. W., Kalmar, J. R., Karrison, T., & Speight, P. M. (2008). Critical evaluation of diagnostic aids for the detection of oral cancer. Oral Oncology, 44(1), 10–22.

    Article  Google Scholar 

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Authors and Affiliations

  1. Bio-Medical Imaging Laboratory (BIOMIL), Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India

    Vijaya Yaduvanshi, R. Murugan & Tripti Goel

Authors
  1. Vijaya Yaduvanshi
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  2. R. Murugan
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  3. Tripti Goel
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Corresponding author

Correspondence to R. Murugan .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Ripon Patgiri

  2. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Anupam Biswas

  3. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Pinki Roy

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Yaduvanshi, V., Murugan, R., Goel, T. (2021). An Automatic Classification Methods in Oral Cancer Detection. In: Patgiri, R., Biswas, A., Roy, P. (eds) Health Informatics: A Computational Perspective in Healthcare. Studies in Computational Intelligence, vol 932. Springer, Singapore. https://doi.org/10.1007/978-981-15-9735-0_8

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