DEEPDAPORCD: Oral Cancer Detection Using Deep Network & Distributed Affinity Propagation

DEEPDAPORCD: Oral Cancer Detection Using Deep Network & Distributed Affinity Propagation

  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : R. Dharani, S. Revathy
DOI :  10.14445/22315381/IJETT-V70I4P225

How to Cite?

R. Dharani, S. Revathy, "DEEPDAPORCD: Oral Cancer Detection Using Deep Network & Distributed Affinity Propagation," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 286-293, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P225

Abstract
One of the most common cancers is oral cancer. Oral cancer appears to be on the rise all around the world. To separate cancerous lesions from contentious and malignant lesions present in the dental cavity, the doctor must go through a high level. Because there is no pain for the sufferer and they mimic several other lesions, cancer`s early stages and eventual manifestations are commonly misunderstood. The research describes a deep learning approach for classifying oral pictures as normal or abnormal. The Distributed Affinity Propagation (AP) algorithm partitioned the diseased patches. Using a deep learning system based on Appearance-based characteristics and Pattern-based features, the segmented cancer spots were further classified as "moderate" or "severe." The deep learning algorithm`s key benefit is that it only requires a small number of oral images for the proposed research`s categorization and diagnostic phases. Recall Rate, Classification Accuracy, Precision Rate, and Error Rate were used to compare the effectiveness of the presented approaches. The study`s findings revealed that a mix of deep learning methods effectively detected oral cancer.

Keywords
Affinity Propagation, Appearance-based feature, Cancer Detection, Improved CNN, Medical Image Processing, Oral. Pattern-based features.

Reference
[1] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, CA: A Cancer J. Clinicians, 68(6) (2018) 394–424.
[2] H. Gelband, P. Jha, R. Sankaranarayanan, and S. Horton, Disease Control Priorities: Cancer, Washington, DC, USA: World Bank, 3 (2015).
[3] J. Rimal, A. Shrestha, I. K. Maharjan, S. Shrestha, and P. Shah, .Risk Assessment of Smokeless Tobacco Among Oral Precancer and Cancer Patients in the Eastern Developmental Region of Nepal,. Asian Pacific J. Cancer Prevention, 20(2) (2019) 411–415.
[4] D. M. Parkin, P. Pisani, J. Ferlay, Estimates of the Worldwide Incidence of Twenty-Five Major Cancers in 1990. Int J Cancer, 80 (1999) 827–41.
[5] S. R. Aziz, Oral Submucous Fibrosis: An Unusual Disease, J N J Dent Assoc, 68 (1997) 17–19.
[6] R. B. Zain, N. Ikeda, I. A. Razak, A National Epidemiological Survey of Oral Mucosal Lesions in Malaysia, Community Dent Oral Epidemiol, 25 (1997) 377–83.
[7] M. D. Rosmai, A. K. Sameemii, A. Basir, I. S. Mazlipahiv, M. D. Norzaidi, The use of Artificial Intelligence to Identify People at Risk of Oral Cancer: Empirical Evidence in Malaysian University, International Journal of Scientific Research in Education, 3(1) (2010) 10-20.
[8] DSVGK. Kaladhar, B. Chandana, P.B. Kumar, Predicting Cancer Survivability Using Classification Algorithms, International Journal of Research and Reviews in Computer Science, 2(2) (2011) 340–343.
[9] M. D. Rosmai, A. Basir, S. A. Kareem, S. M. Ismail, M. D. Norzaidi, Determining the Critical Success Factors of Oral Cancer Susceptibility Prediction in Malaysia Using Fuzzy Models, Sains Malaysiana, 41(5) (2012) 633–640.
[10] J. O. Kang, S. H. Chung, Y. M. Suh, Prediction of Hospital Charges for Cancer Patients With Data Mining Technique, J Kor Soc Med Informatics, 15(1) (2009) 13-23.
[11] N. Sharma, H. Om, Framework for Early Detection and Prevention of Oral Cancer Using Data Mining, International Journal of Advances in Engineering and Technology, 4(2) (2012) 302-310.
[12] Woonggyu Jung, Jun Zhang, Jungrae Chung, Petra Wilder–Smith, Matt Brenner, J. Stuart Nelson and Zhongping Chen, Advances in Oral Cancer Detection Using Optical Coherence Tomography, IEEE Journal of Selected Topics in Quantum Electronics, 11(4) (2005) 811 – 817.
[13] Simon Kent, Diagnosis of Oral Cancer Using Genetic Programming – A Technical Report, CSTR -96-14.
[14] A. Chodorowski, U. Mattsson, T. Gustavsson, Oral Lesion Classification Using True Color Images, Proceedings of SPIE, ISBN. 978081943132, 3661 (1999) 1127 – 1138.
[15] M. Muthu Rama Krishnan, Chandran Chakraborthy, Ajoy Kumar Ray, Wavelet Based Texture Classification of Oral Histopathological Sections, International Journal of Microscopy, Science, Technology, Applications and Education, 897-906.
[16] Neha Sharma, Nigdi Pradhikaran, Akurdi, Comparing the Performance of Data Mining Techniques for Oral Cancer Prediction, Proceedings of the 2011 International Conference on Communication, Computing & Security (ICCCS’11), ISBN: 978-1-4503-0464-1, New York, USA, (2011).
[17] Yung –Nien Sun, Yi-Ying Wang, Shao-Chien Chang, Li-Wha Wu and Sen – Tien Tsai, Color – Based Tumor Segmentation for the Automated Estimation of Oral Cancer Parameters, Microscopy Research and Technique, 73(1) (2010) 5- 13.
[18] Hobdell MH, Oliveria ER, Bautista R, Myburgh NG, Lalloo R, Et Al. Oral Diseases and Socio economic socio economic Status (SES). Br Dent J. 194(2) (2003) 91-96.
[19] Galib, S., F. Islam, M. Abir & H.K. Lee. Computer Aided Detection of Oral Lesions on CT Images, Journal of Instrumentation, 10(12) (2015).
[20] Belvin Thomas, Vinod Kumar, Sunil Saini. Texture Analysis Based Segmentation and Classification of Oral Cancer Lesions in Color Images Using ANN, IEEE International Conference on Signal Processing Computing and Control (ISPCC ) , (2013) 1-5.
[21] Alsmadi, M.K.A Hybrid Fuzzy C-Means and Neutrosophic for Jaw Lesions Segmentation, [Press Release], Retrieved From Https://Doi.Org/10.1016/J. Asej.2016.03.016.(2016).
[22] Banerjee, S., Chatterjee, S., Anura, A., Chakrabarty, J., Pal, M., Ghosh, B., Chatterjee, J. Global Spectral and Local Molecular Connects for Optical Coherence Tomography Features To Classify Oral Lesions Towards Unravelling Quantitative Imaging Biomarkers, RSC Advances. 6(9) (2016) 7511- 7520.
[23] Tanupriya Choudhury, Vivek Kumar, Darshika Nigam, Bhaskar Mandal. Intelligent Classification of Lung Oral Cancer Through Diverse Data Mining Algorithms, International Conference on Microelectronics and Telecommunication Engineering. (2016).
[24] Yi-Ying Wang, Shao-Chien Chang, Li-Wha Wu, Sentien Tsai and Yung-Nien Sun. A Color-Based Approach for Automated Segmentation in Tumor Tissue Classification, Conference of the IEEE EMBS Citéinternationale, Lyon, France. (2016).
[25] Chang, S.W., Abdul-Kareem, S., Merican, AF, Zain, RB Oral Cancer Prognosis Based on Clinicopathologic and Genomic Markers Using A Hybrid of Feature Selection and Machine Learning Method, BMC Bioinformatics, 14(1) (2016)
[26] J. G. Doss, W. M. Thomson, B. K. Drummond, and R. J. R. Latifah, Validity of the FACT-H&N (V 4.0) Among Malaysian Oral Cancer Patients,’’ Oral Oncol., 47(7) (2011) 648–652.
[27] H. Amarasinghe, R. D. Jayasinghe, D. Dharmagunawardene, M. Attygalla, P. A. Scuffham, N. Johnson, and S. Kularatna, `Economic Burden of Managing Oral Cancer Patients in Sri Lanka: A Cross-Sectional Hospital-Based Costing Study, BMJ Open, 9(7) (2019).Art. No. E027661.
[28] R. D. Jayasinghe, L. P. G. Sherminie, H. Amarasinghe, and M. A. Sitheeque, Level of Awareness of Oral Cancer and Oral Potentially Malignant Disorders Among Medical and Dental Undergraduates, Ceylon Med. J., 61(2) (2016) 77.
[29] O. Kujan, A.-M. Glenny, R. Oliver, N. Thakker, and P. Sloan, Screening Programmes for the Early Detection and Prevention of Oral Cancer, Austral. Dental J., 54(2) (2009) 170–172.