Cervical cancer is the second most common gynaecologic cancer worldwide. Unlike the other cancers it does not show any symptoms in its earlier stage which causes mortality among women. It takes 8 to 10 years to develop from precancerous to severe stage. The important reasons for higher cervical cancer in developing countries are lack of resources, lack of effectual screening programs and inadequately organized health system aimed for detecting precancerous condition before they progress to persistent cancer and also 80% of cervical cancers are incurable at the time of detection due to their advanced stage. Therefore, early detection of cervical cancer is more important for reducing the mortality rate of the women. Thus, the aim of this paper is to investigate about the classification of cervical cell as Normal Cell or Abnormal Cell by using individual feature extraction method and joining individual feature extraction features method with the classification technique. In this paper, we proposed three novel feature extraction methods. From that three, two were individual feature extraction methods, they are Extending Enriched Texton Co-Occurrence Matrix (EETCM) and Effective Extending Enriched Texton Co-Occurrence Matrix (EEETCM) and the remained one was joining individual feature extraction features method named as Concatenated Feature Extraction (CFE). The CFE method represents joining all the individual feature extraction methods of EETCM, EEETCM features into one feature to assess their joint performance. Then these three feature extraction methods are tested over two classifiers: Kernel Support Vector Machine (K-SVM) and Support Vector Machine (SVM). This Examination was conducted over a set of single cervical cell based pap smear images. The dataset contains two classes of images, with a total of 952 images. The distribution of number of images per class is not uniform. Then, the performance of the proposed system was evaluated in terms of the statistical parameters of sensitivity, specificity & accuracy in both the individual feature extraction & classification combinations and joining all the individual feature extraction features method and classification combinations. Hence, the performance of individual combination method described, the proposed EEETCM features with Kernel SVM Classifier combination had given the better results than the other combinations such as EEETCM with SVM Classifier, EETCM with Kernel SVM Classifier, EETCM with SVM Classifier. Then the performance of joining all the individual feature extraction method and classification combination described, proposed Concatenated Feature Extraction (CFE) with Kernel SVM Classifier had given the better results than CFE with SVM Classifier and all other individual feature extraction and classification combinations.
S. Athinarayanan, Dr. M. V. Srinath, R. Kavitha
Cervical Cancer, Feature Extraction, Classification
- "Cervical Cancer Treatment (PDQ®)". NCI. 2014-03-14. June2014.
- "Defining Cancer". National Cancer Institute, June 2014.
- Tarney, CM; Han, J (2014). "Postcoital bleeding: a review on etiology, diagnosis, and management.". Obstetrics and Gynecology International. 2014: 192087.doi:10.1155/2014/192087. PMID 25045355.
- Kumar V, Abbas AK, Fausto N, Mitchell RN (2007). Robbins Basic Pathology (8th ed.). Saunders Elsevier. pp. 718–721. ISBN 978-1-4160-2973-1.
- Kufe, Donald (2009). Holland-Frei cancer medicine. (8th ed.). New York: McGraw-Hill Medical. p. 1299. ISBN 9781607950141.
- World Cancer Report 2014. World Health Organization. 2014. pp. Chapter 5.12. ISBN 9283204298.
- Dunne, EF; Park, IU (Dec 2013). "HPV and HPV-associated diseases.". Infectious Disease Clinics of North America. 27 (4): 765–78. doi:10.1016/j.idc.2013.09.001.PMID 24275269.
- "Cervical Cancer Treatment (PDQ®)". National Cancer Institute. 2014-03-14. Retrieved 25 June 2014.
- "What is cervical screening". National Screening Unit, Government of New Zealand. 27 November 2014.
- Levels of Disease Prevention. (2007, April 24). Retrieved March 16, 2014, from Centers for Disease Control and Prevention website: "Archived copy". Archived from the original on 2014-02-26. Retrieved 2015-08-23.
- Quinn, M; Babb, P; Jones, J; Allen, E (1999). "Effect of screening on incidence of and mortality from cancer of cervix in England: evaluation based on routinely collected statistics.". BMJ. 318: 904–8. doi:10.1136/bmj.318.7188.904. PMC 27810 .PMID 10102852.
- World Health Organization (2014). Comprehensive Cervical Cancer Control: A Guide to Essential Practice. WHO.
- Milan Sonka, Vaclav Hlavac, Roger Boyle, "Image Processing, Analysis and Machine Vision”, pp: 56-11.
- Yogamangalam, B.Karthikeyan, "Segmentation Techniques Comparison in Image Processing”, ISSN : 0975-4024 Vol 5 No 1 Feb-Mar 2013, International Journal of Engineering and Technology (IJET), 307-313.
- Lu & Q. Weng (2007) A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, 28:5, 823-870, DOI: 10.1080/01431160600746456.
- Daniel X. Le, George R. Thoma, Harry Wechsler . Classification of binary document images into textual or nontextual data blocks using neural network models, Springer Verlag publication. 1995; 8: 289-16.
- Soumya M. K, Sneha K and Arunvinodh C, "Cervical Cancer Detection and Classification using Texture Analysis”, Biomedical and Pharmacology Journal, ISSN:0974-6242. E-ISSN:2456-261.
- Ashok Int. Journal of Engineering Research and Applications, ISSN: 2248-9622, Vol. 6, Issue 1, (Part - 1) January 2016, pp.94-99.
- JieSu,1 XuanXu, YongjunHe and JinmingSong. "Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System”, Hindawi Publishing Corporation Analytical Cellular Pathology Volume 2016, Article ID 9535027, 11 pages http://dx.doi.org/10.1155/2016/9535027.
- Siti Noraini Sulaimana, Nor Ashidi Mat-Isab, Nor Hayati Othmanc, Fadzil Ahmada. "Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System”, 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems. 60 (2015) 750 – 759, 1877-0509 © 2015.
- Deepak et al., J Cytology Histology, "Computer Assisted Pap Smear Analyser for Cervical Cancer Screening using Quantitative Microscopy”, 2015, S:3 http://dx.doi.org/10.4172/2157-7099.S3-010.
- Sreedevi et al. "Papsmear Image based Detection of Cervical Cancer”, International Journal of Computer Applications (0975 – 8887) Volume 45– No.20, May 2012.
- Anousouya devi et al., "Detection of Cervical Cancer using the Image Classification Algorithms" I J C T A, 9(3), 2016, pp. 153-166 © International Science Press.
- Sakthi priya, "Cervical Cancer Screening and Classification Using Acoustic Shadowing”, International Journal of Innovative Research in Computer and Communication Engineering, Volume 1, Issue 8, October 2013.
- Karthigai Lakshmi et al. "Feature Extraction and Feature Set Selection for Cervical Cancer Diagnosis”, Indian Journal of Science and Technology, Vol 9(19), DOI: 10.17485/ijst/2016/v9i19/93881, May 2016.
- Nazahah Mustafa et. Al, "Cervical Cancer Diagnostic System using HNN”, IJSSST, Vol. 9, No. 2, May 2008, ISSN: 1473-804x online, 1473-8031.
- Demirkaya O. "Anisotropic diffusion filtering of PET attenuation data to improve emission images”, Phys MED Biol vol.47(20): N271-8.2002.
- Julesz, Textons, The elements of Texture Perception and their interactions, Nature 290 (5802) (1981) 91-97.
- Liu G.-H., Zhang L., Hou Y.K., Z.Y. Li and Yan J.Y. g 2010 Image retrieval based on multi- texton histogram Pattern Recognition 43 2380-2389.
- Zhang J., Liu Y., Cervical Cancer Detection Using SVM Based Feature Screening, Proc of the 7th Medical Image Computing and Computer-Assisted Intervention,vol. 2, pp.873-880,2004.
- Zhang K., CAO H.X., Yan H., Application of support vector machines on network abnormal intrusion detection. Application Research of Computers, vol.5, pp.98-100, 2006.
- Luz Helena Camargo Casallas (2012) Classification of squamous cell cervical cytology, Master’s Thesis, Faculty of Medicine – Engineering Faculty, Universidad Nacional de Colombia, Bogota D.C Colombia.
- Yung-Fu Chen, Po-Chi Huang, Ker-Cheng Lin, Hsuan Hung Lin, Li-En Wang, Chung-chuan Cheng, Tsung-Po Chen, Yung-Kuan Chan, John Y. Chiang (2013), Semiautomatic segmentation and classification of Pap smear cells. IEEE Journal of Biomedical and Health Informatics, pp 1–15.
- Ramin Moshavegh, Babak Ehteshami Bejnordi (2013) Chromatin pattern analysis of cell nuclei for improved cervical cancer detection, Master’s Thesis in Biomedical Engineering, Department of Signal and systems, Chalmers University of Technology, Sweden.
- Theodoridis and K. Koutroumbas (2009) Pattern Recognition, 4th ed. China Machine Press.
- Simon Haykin (2009) Neural networks and learning machines, 3rd ed. China Machine press, Pearson education Asia Ltd.
- Wen Zhu, Nancy Zeng, Ning Wang, "Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS Implementations", Proceedings of the SAS Conference, Baltimore, Maryland, pages: 9, 2010.
- Ounpraseuth S, Lensing SY, Spencer HJ, Kodell RL. "Estimating misclassification error: a closer look at cross-validation based methods”, BMC Res Notes (28) : 5:656,2012.
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Cite This Article
S. Athinarayanan, Dr. M. V. Srinath, R. Kavitha, "Detection and Classification of Cervical Cancer in Pap Smear Images using EETCM, EEETCM & CFE methods based Texture features and Various Classification Techniques", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 5, pp.533-549 , September-October-2016.
URL : http://ijsrset.com/IJSRSET173177.php