ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Cervical Cancer Detection and Classification by using Effectual Integration of Directional Gabor Texture Feature Extraction and Hybrid Kernel Based Support Vector Classification


Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, India
2 Department of Computer Applications, The MDT Hindu College, India
3 Department of Computer Science and Engineering, Universal College of Engineering and Technology, India
     

   Subscribe/Renew Journal


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.
Subscription Login to verify subscription
User
Notifications
Font Size

  • 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.


Abstract Views: 194

PDF Views: 0




  • Cervical Cancer Detection and Classification by using Effectual Integration of Directional Gabor Texture Feature Extraction and Hybrid Kernel Based Support Vector Classification

Abstract Views: 194  |  PDF Views: 0

Authors

S. Athinarayanan
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, India
K. Navaz
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, India
R. Kavitha
Department of Computer Applications, The MDT Hindu College, India
S. Sameena
Department of Computer Science and Engineering, Universal College of Engineering and Technology, India

Abstract


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