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Digital Image Based Segmentation and Classification of Tongue Cancer Using CNN

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Abstract

Due to the change in life style after covid-19 there is demand for non-invasive contact less healthcare monitoring systems. In India oral cancer rate are increasing and becoming the community health issue with high mortality rate. Mortality rate can be reduced by identification of disease at initial stages. The major hindrance in disease identification is availability of dedicated hardware at remote and identification at initial stage. Research is going on to make the device portable and less costly using digital images. Also, research is going on to have hybrid algorithm which can segment smaller area abnormal area and classify reliably. This paper focuses on tongue cancer which is one of the types of oral cancer and presents comparison of hybrid algorithm using firefly and watershed transformation to segment smaller area using digital images which reduces cost of dedicated hardware required. 150 digital images are used which are available on internet or provided by cancer hospital for analysis and classification using CNN along with augmentation. 90.48% accuracy is achieved and desirable results are obtained using hybrid algorithm being used.

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Data Availability

Material is provided, data will be available from the corresponding author on reasonable request.

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Code will be made available from the corresponding author on reasonable request.

References

  1. Anuradha, K. (2013). Statistical feature extraction to classify oral cancers. Journal of Global Research in Computer Science, 4(2), 8–12.

    MathSciNet  Google Scholar 

  2. Shamim, M. Z. M., Syed, S., Shiblee, M., Usman, M., Ali, S. J., Hussein, H. S., & Farrag, M. (2022). Automated detection of oral pre-cancerous tongue lesions using deep learning for early diagnosis of oral cavity cancer. The Computer Journal, 65(1), 91–104.

    Article  Google Scholar 

  3. Kim, M., Cobbin, D., & Zaslawski, C. (2008). Traditional Chinese medicine tongue inspection: An examination of the inter-and intra-practitioner reliability for specific tongue characteristics. The Journal of Alternative and Complementary Medicine, 14(5), 527–536.

    Article  Google Scholar 

  4. Cai, Y. (2002). A novel imaging system for tongue inspection. In IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No. 00CH37276) (Vol. 1, pp. 159–163). IEEE.

  5. Zhang, D., Zhang, H., Zhang, B., Zhang, D., Zhang, H., & Zhang, B. (2017). A snake-based approach to automated tongue image segmentation. Tongue Image Analysis, 71-88.

  6. Chiu, C. C. (2000). A novel approach based on computerized image analysis for traditional Chinese medical diagnosis of the tongue. Computer Methods and Programs in Biomedicine, 61(2), 77–89.

    Article  Google Scholar 

  7. Jang, J. H., Kim, J. E., Park, K. M., Park, S. O., Chang, Y. S., & Kim, B. Y. (2002). Development of the digital tongue inspection system with image analysis. In Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society Engineering in Medicine and Biology (Vol. 2, pp. 1033–1034). IEEE

  8. Zhang, H. Z., Wang, K. Q., Zhang, D., Pang, B., & Huang, B. (2006). Computer aided tongue diagnosis system. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference (pp. 6754–6757). IEEE

  9. Tania, M. H., Lwin, K., & Hossain, M. A. (2018). Advances in automated tongue diagnosis techniques. Integrative Medicine Research, 8(1), 42–56.

    Article  Google Scholar 

  10. Lo, L. C., Cheng, T. L., Chen, Y. J., Natsagdorj, S., & Chiang, J. Y. (2015). TCM tongue diagnosis index of early-stage breast cancer. Complementary therapies in medicine, 23(5), 705–713.

    Article  Google Scholar 

  11. Lo, L. C., Chen, Y. F., Chen, W. J., Cheng, T. L., & Chiang, J. Y. (2012). The study on the agreement between automatic tongue diagnosis system and traditional Chinese medicine practitioners. Evidence-Based Complementary and Alternative Medicine, 2012, 1–9.

    Google Scholar 

  12. Jung, C. J., Kim, K. H., Jeon, Y. J., & Kim, J. (2014). Improving color and shape repeatability of tongue images for diagnosis by using feedback gridlines. European Journal of Integrative Medicine, 6(3), 328–336.

    Article  Google Scholar 

  13. Kawanabe, T., Kamarudin, N. D., Ooi, C. Y., Kobayashi, F., Mi, X., Sekine, M., & Hanawa, T. (2016). Quantification of tongue colour using machine learning in Kampo medicine. European Journal of Integrative Medicine, 8(6), 932–941.

    Article  Google Scholar 

  14. Uma Devi, G., & Ravi, T. (2015). Disease diagnosis for various signs using tongue color image segmentation. Australian Journal of Basic and Applied Sciences, 9(10), 341–348.

    Google Scholar 

  15. Wang, Y., Yang, J., & Zhou, Y. (2005). Tongue image color recognition in traditional Chinese medicine. Sheng wu yi xue gong cheng xue za zhi = Journal of Biomedical Engineering = Shengwu yixue gongchengxue zazhi, 22(6), 1116–1120.

    Google Scholar 

  16. Kamarudin, N. D., Ooi, C. Y., Kawanabe, T., Odaguchi, H., & Kobayashi, F. (2017). A fast SVM-based tongue’s colour classification aided by k-means clustering identifiers and colour attributes as computer-assisted tool for tongue diagnosis. Journal of healthcare engineering, 2017, 7460168.

    Article  Google Scholar 

  17. Steinley, D., & Brusco, M. J. (2011). Choosing the number of clusters in Κ-means clustering. Psychological methods, 16(3), 285.

    Article  Google Scholar 

  18. Zhou, L., & Li, L. (2018). Improvement of the firefly-based K-means clustering algorithm. In Proceedings of the 2018 International Conference on Data Science (pp. 157-162).

  19. Kucukyilmaz, T., & University of Turkish Aeronautical Association. (2014). Parallel k-means algorithm for shared memory multiprocessors. Journal of Computer and Communications, 2(11), 15.

    Article  Google Scholar 

  20. Sharma, J., Rai, J. K., & Tewari, R. P. (2015). A combined watershed segmentation approach using k-means clustering for mammograms. In 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 109- 113). IEEE.

  21. Zheng, X., Lei, Q., Yao, R., Gong, Y., & Yin, Q. (2018). Image segmentation based on adaptive K-means algorithm. EURASIP Journal on Image and Video Processing, 2018(1), 1–10.

    Article  Google Scholar 

  22. Hrosik, R. C., Tuba, E., Dolicanin, E., Jovanovic, R., & Tuba, M. (2019). Brain image segmentation based on firefly algorithm combined with k-means clustering. Studies in Informatics Control, 28, 167–176.

    Google Scholar 

  23. Sharma, A., Chaturvedi, R., Dwivedi, U. K., Kumar, S., & Reddy, S. (2018). Firefly algorithm based Effective gray scale image segmentation using multilevel thresholding and Entropy function. International Journal of Pure and Applied Mathematics, 118(5), 437–443.

    Google Scholar 

  24. Pambudi, E. A., Andono, P. N., & Pramunendar, R. A. (2018). Image segmentation analysis based on K-means PSO by using three distance measures. ICTACT Journal on Image and Video Processing, 9(1), 1821–1826.

    Article  Google Scholar 

  25. Zhu, J., Styler, W., & Calloway, I. C. (2018). Automatic tongue contour extraction in ultrasound images with convolutional neural networks. The Journal of the Acoustical Society of America, 143(3), 1966–1966.

    Article  Google Scholar 

  26. Hu, M. C., Zheng, G. Y., Chen, Y. T., & Lan, K. C. (2014). Automatic tongue diagnosis using a smart phone. In 2014 IEEE International Conference on Systems, Man, and Cybernetics.

  27. https://www.healthline.com/health/oral-cancer/tongue-cancer#symptoms

  28. Cui, Z., Zhang, H., Zhang, D., Li, N., & Zuo, W. (2013). Fast marching over the 2D Gabor magnitude domain for tongue body segmentation. EURASIP journal on advances in signal processing, 2013(1), 1–13.

    Article  Google Scholar 

  29. Miryala, D., Parvataneni, P., & Aliperi, G. (2014). Computer aided image enhancement of tongue for diagnosis in ayurvedic medical treatment. Applied Medical Informatics, 34(1), 46–56.

    Google Scholar 

  30. Haron, N., Zain, R. B., Nabillah, W. M., Saleh, A., Kallarakkal, T. G., Ramanathan, A., & Cheong, S. C. (2017). Mobile phone imaging in low resource settings for early detection of oral cancer and concordance with clinical oral examination. Telemedicine and e-Health, 23(3), 192–199.

    Article  Google Scholar 

  31. https://oralcancerfoundation.org/dental/oral-cancer-images/

  32. Jung, C. J., Jeon, Y. J., Kim, J. Y., & Kim, K. H. (2012). Review on the current trends in tongue diagnosis systems. Integrative Medicine Research, 1(1), 13–20.

    Article  Google Scholar 

  33. Zhou, Z., Peng, D., Gao, F., & Lu, L. (2019). Medical diagnosis algorithm based on tongue image on mobile device. Journal of Multimedia Information System, 6(2), 99–106.

    Article  Google Scholar 

  34. Pahadiya, P., Vijay, D. R., kumar Gupta, K., Saxena, S., & Tandon, R. (2020). A Novel method to get proper tongue image acquisition and thresholding for getting area of interest. International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN, 2278-3075

  35. Abbas, O. A. (2008). Comparisons between data clustering algorithms. International Arab Journal of Information Technology (IAJIT), 5(3), 320–325.

    Google Scholar 

  36. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE

  37. Shi, Y., Liu, H., Gao, L., & Zhang, G. (2011). Cellular particle swarm optimization. Information Sciences, 181(20), 4460–4493.

    Article  MathSciNet  MATH  Google Scholar 

  38. Chen, K., Zhou, Y., Zhang, Z., Dai, M., Chao, Y., & Shi, J. (2016). Multilevel image segmentation based on an improved firefly algorithm. Mathematical Problems in Engineering. https://doi.org/10.1155/2016/1578056

    Article  Google Scholar 

  39. Abdallah, Y., Alkhir, M., & Algaddal, A. (2015). Improvement of brain tumors detection using markers and boundaries transform. International Journal of Science and Research (IJSR), 4(1), 2372–2378.

    Google Scholar 

  40. Aydilek, I. B. (2018). A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Applied Soft Computing, 66, 232–249.

    Article  Google Scholar 

  41. Rogowska, J. (2000). Overview and fundamentals of medical image segmentation. Handbook of medical imaging (pp. 69–85). Elsevier. https://doi.org/10.1016/B978-012077790-7/50009-6

    Chapter  Google Scholar 

  42. Pahadiya, M. P., Vijay, R., Saxena, M. S., Gupta, M. K. K., & Shahapurkar, T. (2021). CNN based automatic tongue cancer detection using hybrid k-mean and watershed transformation. Design Engineering, 1864–1879

  43. Kirubabai, M. P., & Arumugam, G. (2021). Deep learning classification method to detect and diagnose the cancer regions in oral MRI images. Medico Legal Update, 21, 462–468.

    Google Scholar 

  44. Pahadiya, M. P., Vijay, R., Gupta, M. K. K., & Saxena, M. S. (2021). Automatic identification of abnormal tongue image using CNN with K-mean and hybrid firefly algorithm. Annals of the Romanian Society for Cell Biology., 25, 7322–7336.

    Google Scholar 

  45. Gupta, K. K., Vijay, R., Pahadiya, P., & Saxena, S. (2022). Use of novel thermography features of extraction and different artificial neural network algorithms in breast cancer screening. Wireless Personal Communications, 123, 1–30.

    Article  Google Scholar 

  46. Anuradha, K., & Sankaranarayanan, K. (2014). A review on computer aided detection techniques of oral cancer. International Journal of Computational Science and Engineering, 2(3), 109–114.

    Google Scholar 

  47. Ayumi, V., Rere, L. R., Fanany, M. I., & Arymurthy, A. M. (2016). Optimization of convolutional neural network using microcanonical annealing algorithm. In 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 506–511). IEEE

  48. Khalid Nazim Abdul Sattar. (2020). TADOC : Tool for automated detection of oral cancer. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/IJACSA.2020.0110364

    Article  Google Scholar 

  49. Anantharaman, R., Velazquez, M., & Lee, Y. (2018). Utilizing mask R-CNN for detection and segmentation of oral diseases. In 2018 IEEE international conference on bioinformatics and biomedicine (BIBM) (pp. 2197-2204). IEEE

  50. Islam, R., Imran, S., Ashikuzzaman, M., & Khan, M. M. A. (2020). Detection and classification of brain tumor based on multilevel segmentation with convolutional neural network. Journal of Biomedical Science and Engineering, 13(4), 45–53.

    Article  Google Scholar 

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Acknowledgements

Apollo cancer Hospital for providing digital images.

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Authors and Affiliations

  1. Institute of Advance Computing, SAGE University, Indore, India

    Pallavi Pahadiya

  2. Electronics Department, Banasthali Vidhyapith, Niwai, Rajasthan, India

    Ritu Vijay & Shivani Saxena

  3. CS-AI Department, Noida Institute of Engineering and Technology, Greater Noida, India

    Kumod Kumar Gupta

  4. Shapurkar Clinic, Indore BDMS Nashik University Maharashtra, Nanada Nagar, Indore, India

    Tushar Shahapurkar

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  1. Pallavi Pahadiya
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Correspondence to Pallavi Pahadiya.

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Pahadiya, P., Vijay, R., Gupta, K.K. et al. Digital Image Based Segmentation and Classification of Tongue Cancer Using CNN. Wireless Pers Commun 132, 609–627 (2023). https://doi.org/10.1007/s11277-023-10626-7

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