The week's pick
Random Articles
Reseach Article
Pap Smear Images Classification for Early Detection of Cervical Cancer
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 118 - Number 7 |
| Year of Publication: 2015 |
| Authors: Ayubu Hassan Mbaga, Pei Zhijun |
10.5120/20756-3159
|
Ayubu Hassan Mbaga, Pei Zhijun . Pap Smear Images Classification for Early Detection of Cervical Cancer. International Journal of Computer Applications. 118, 7 ( May 2015), 10-16. DOI=10.5120/20756-3159
Abstract
In this presents the analyses of the Pap smear cervical cell images for cervical screening and detection. Initially preprocessed the cell images to remove unwanted noises, Followed by extraction of the cell from the background to obtain the cytoplasm and nucleus of the cell which is the region of interest. It is the only parts of the cell which can be used to differentiate normal cell from abnormal one. 20 salient features were extracted for training of support vector machine. SVM-RFE is used for features selection; the RFE algorithm removes unimportant features based on backward sequential selection by iteratively deleting one feature at a time, resulting in suboptimal combination of r(r
References
- International Agency for Research on Cancer, World Health Organization, available [online] Available from http://screening. iarc. fr/cervicalindex. php [accessed 10 September 2014]
- Eric Martin (2003) Pap-smear classification, Technical University of Denmark-DTU, Master's Thesis.
- M. Mohideen Fatima alias Niraimathi, V. Seenivasagam (2012) A hybrid image segmentation of cervical cells by bi-group enhancement and scan line filling. International Journal of Computer Science and Information Technology and Security, 2 (2), 368–375.
- Jonus Norup (2005) Classification of Pap-smear data by transductive neuro-fuzzy methods, Technical University of Denmark-DTU, Master's Thesis.
- Hana Sarbortova (2013) Detection of cervical cancer in Pap smear images, University of Wisconsin-Madison, Final Project Report
- Ash Genctav, Selim Aksoy, Sevgen Onder (2012) Unsupervised segmentation and classification of cervical cell images. Pattern Recognition, 45, 4151–4168
- Marina E. Plissiti, Christophoros Nikou (2012) Cervical cell classification based exclusively on nucleus, ICIAR2 2012, part ll LNCS, pp. 483–470.
- Nor Ashidi Mat Isa, Nazahah Mustafa, Kamal Zuhairi Zamli, Mohd Yusoff Mashor (2006) Improvement of Contrast Enhancement Technique for cervical cell of Pap smear images by reducing the effects of unwanted background information, IEEE.
- Rafael C. Gonzalez, Richard E. Woods (2010) Digital image processing. 3rd ed. Beijing, Pearson Education Asia Ltd.
- Jianchang Mao, Anil K. Jain (1995) Artificial neural networks for feature extraction and multivariate data projection. IEEE Transactions on Neural Networks, 6 (2). pp. 296-317.
- Edwin Jayasingh, S. Allwin (2013) Detection of cancer in pap smear cytological images using bag of texture features. IOS Journal of Computer Engineering (IOS-JCE), 11 (1), pp. 01–07.
- 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.
- I. Guyon, J. Weston, S. Barnhill, V. Vapnik (2002) Gene selection for cancer classification using Support Vector Machines. Machine Learning 46, pp. 389–422.
- 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.
- 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
- S. 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.
Index Terms
Keywords