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Navaz, K.
- Cervical Cancer Detection and Classification by using Effectual Integration of Directional Gabor Texture Feature Extraction and Hybrid Kernel Based Support Vector Classification
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Authors
Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Applications, The MDT Hindu College, IN
3 Department of Computer Science and Engineering, Universal College of Engineering and Technology, IN
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Applications, The MDT Hindu College, IN
3 Department of Computer Science and Engineering, Universal College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 3 (2019), Pagination: 1935-1939Abstract
Planning of invigorating representation is a troublesome and testing process because of the unpredictability of the images and absence of models of the life systems that thoroughly catches the reasonable expressions in each structure. Cervical malignant growth is one of the noteworthy reasons for death among different kinds of the diseases in women around the world. Genuine and auspicious determination can keep the life to some dimension. Therefore, we have proposed a computerized dependable framework for the analysis of the cervical malignancy utilizing surface highlights and machine learning calculation in Pap smear images, it is extremely advantageous to anticipate disease, likewise expands the dependability of the determination. Proposed framework is a multi-organize framework for cell nucleus extraction and disease finding. To begin with, clamor expulsion is performed in the preprocessing venture on the Pap smear images. Exterior highlights are separated from these demand free Pap smear images. Next period of the proposed framework is classification that depends on these separated highlights, SVM classification is utilized. Over 94% exactness is accomplished by the classification stage, demonstrated that the proposed calculation precision is great at recognizing the disease in the Pap smear images.Keywords
Cervical Cancer, Feature Extraction, DGTF, Classification, Hybrid Kernel SVM.
References
- Hayder K. Fatlawi, “Enhanced Classification Model for Cervical Cancer Dataset based on Cost Sensitive Classifier”, International Journal of Computer Techniques, Vol. 4, No. 4, pp. 1-8, 2017.
- S. Athinarayanan, M.V. Srinath and R. Kavitha, “Computer Aided Diagnosis for Detection and Stage Identification of Cervical Cancer by using Pap Smear Screening Test Images”, ICTACT Journal on Image and Video Processing, Vol. 6, No. 4, pp. 1244-1251, 2016.
- M. Martinez, L.E. Sucar, H.G. Acosta and N. Cruz, “Bayesian Model Combination and Its Application to Cervical Cancer Detection”, Proceedings of Ibero-American Conference on Artificial Intelligence, pp. 622-631, 2006.
- G. Sun, S. Li, Y. Cao and F. Lang, “Cervical Cancer Diagnosis based on Random Forest”, International Journal of Performability Engineering, Vol. 13, No. 4, pp. 446-457, 2017.
- S. Athinarayanan and M.V. Srinath, “Classification of Cervical Cancer Cells in PAP SMEA Screening Test”, ICTACT Journal on Image and Video Processing, Vol. 6, No. 4, pp. 1234-1238, 2016.
- S. Aswathy, M.A. Quereshi, B. Kurian and K. Leelamoni, “Cervical Cancer Screening: Current Knowledge and Practice among Women in a Rural Population of Kerala, India”, Indian Journal of Medical Research, Vol. 136, No. 2, pp. 205-210, 2012.
- M. Anousouya Devi, S. Ravi and J.V.S. Punitha, “Detection of Cervical Cancer using the Image Classification Algorithms”, International Journal of Circuit Theory and Applications, Vol. 9, No. 3, pp. 153-166, 2016.
- G. Peng, B. Koyel and R.S. Joe, “Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis with Fusion-Based Classification”, IEEE Journal of Biomedical and Health Informatics, Vol. 20, No. 6, pp. 1595-1607, 2016.
- K. Thangavel, P.P. Jaganathan and P.O. Easmi, “Data Mining Approach to Cervical Cancer Patients Analysis using Clustering Technique”, Asian Journal of Information Technology, Vol. 5, No. 4, pp. 413-417, 2006.
- S. Athinarayanan and M.V. Srinath, “Robust and Efficient Diagnosis of Cervical Cancer in Pap Smear Images using Texture Features with RBF and Kernel SVM Classification”, ARPN Journal of Engineering and Applied Sciences, Vol. 11, No. 7, pp. 4504-4515, 2016.
- S. Athinarayanan and M.V. Srinath, “Severity Analysis of Cervical Cancer in PAP SMEAR Images by using EEETCM, ERSTCM and CFE Method based Texture Features and Hybrid Kernel Based Support Vector Machine Classifier”, International Journal of Advanced Research, Vol. 4, No. 11, pp. 2751-2764, 2016.
- S. Athinarayanan and M.V. Srinath, “Effective Detection of Cervical Cancer During Pap Smear Screening Test”, Elysium Journal of Engineering Research and Management, Vol. 1, No. 1, pp. 10-14, 2014.
- S. Athinarayanan and M.V. Srinath, “Multi Class Cervical Cancer Classification by using ERSTCM, EMSD and CFE Methods Based Texture Features and Fuzzy Logic Based Hybrid Kernel Support Vector Machine Classifier”, IOSR Journal of Computer Engineering, Vol. 19, No. 1, pp. 23-34, 2017.
- Effective Image Processing Techniques Based Iris Attendance System
Abstract Views :204 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, IN
3 Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, IN
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, IN
3 Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 2 (2019), Pagination: 2071-2075Abstract
In this paper, the iris prediction gives an incipient conception for Biometric benchmark process. Biometrics is the most secure and utilize-cordial benchmark implement. Iris perception technology contains pattern perception and optics method. It identifies an individual person by utilizing their individual physical characteristics. Iris perception system is very wide compared with other biometric systems. The main wholesomeness of iris perception system is its stability and uniqueness that results in a single enrolment for the lifetime. It provides increasingly varies than the fingerprint and the other biometrics systems, where the information cannot be stolen.Keywords
Iris Prediction System, Pattern Analysis, Biometric Authentication, Human Eye.References
- Gajendra Singh Chandel and Ankesh Bhargava, “Identification of People by Iris Recognition”, International Journal of Science and Modern Engineering, Vol. 1, No. 4, pp. 1-12, 2013.
- J. Daugman, “How Iris Recognition Works”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No. 1, pp. 21-29, 2005.
- Richard Yew Fatt Ng, Yong Hour Tay and Kai Ming Mok, “A Review of Iris Recognition Algorithm”, Proceedings of International Symposium on Information Technology, pp. 26-32, 2008.
- Seifedine Kadry and Khaled Smaili, “A Design and Implementation of a Wireless Iris Recognition Attendance Management System”, Information Technology and Control, Vol. 36, No. 3, pp. 323-329, 2007.
- M. Mattam, S.R.M. Karumuri and S.R. Meda, “Architecture for Automated Student Attendance”, Proceedings IEEE 4th International Conference on Technology for Education, pp. 164-167, 2012.
- Iris Recognition, Available at: http://en.wikipedia.org/wiki/Iris_recognition.
- Ujwalla Gawande, Mukesh Zaveri and Avichal Kapur, “Improving Iris Recognition Accuracy by Score Based Fusion Method”, International Journal of Advancements in Technology, Vol. 1, No. 1, pp. 1-8, 2010.
- R.M. Bolle, J.H. Connell, S. Pankanti, N.K. Ratha and A.W. Senior, “Guide to Biometrics”, Springer, 2003.
- N. Sudha, N.B. Puhan, H. Xia and X. Jiang, “Iris Recognition on Edge Maps”, IET Computer Vision, Vol. 3, No. 1, pp. 1-7, 2009.