Effective Feature Selection for Pre-Cancerous Cervix Lesions Using Artificial Neural Networks

Authors

Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran,

Abstract

Since most common form of cervical cancer starts with pre-cancerous changes, a flawless detection of these changes becomes an important issue to prevent and treat the cervix cancer. There are 2 ways to stop this disease from developing. One way is to find and treat pre-cancers before they become true cancers, and the other is to prevent the pre-cancers in the first place. The presented approach uses precancerous images which are taken from a digital colposcope, and a set of texture and color features is extracted which includes low and high grade SIL (Squamous Interepithelial Lesion ) .After extracting, features are fed to a classifier, which could be KNN,RBF,MLP and Neuro-Fuzzy network and after training effective features are selected using UTA algorithm for each classifier individually. Finally, results come in a comparison table, show that the landa fourteenth, theta-x and together with Neuro-fuzzy classifier have the best overall performance. This approach has an acceptable and simple early diagnosis of cervix cancer and may have found clinical application

Keywords


[1]
K. Tumer, N Ramanujam, J. Ghosh, R. Kortum, “Ensembles of Radial Basis Function Networks for Spectroscopic Detection of Cervical Precancer”, IEEE Transactions on Biomedical Engineering, Vol.45, No.8, 1998.
[2]
I. Claude, R. Winzenrieth, P. Pouletaut, J. Charles Boulanger, ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1, Page 10771.
[3]
B. Tulpule, Sh. Yang, Y. Srinivasan, S. Mitra, B. Nutter, “Segmentation and Classification of Cervix Lesions by Pattern and Texture Analysis”, The 14th IEEE International Conference Fuzzy Systems FUZZ '05, 2005.
[4]
Y. Artan, X. Huang , ”Combining Multiple 2ν-SVM Classifiers for Tissue Segmentation”, 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.488-491, 2008.
[5]
Y. Srinivasan, E. Corona, B. Nutter, S. Mitra, S.Bhattacharya, “A Unified Model-Based Image Analysis Framework for Automated Detection of Precancerous Lesions in Digitized Uterine Cervix Images”, IEEE Journal of Selected Topics in Signal Processing, Vol.3, No.1, 2009.
[6]
A. Das, A. Kar, D. Bhattacharyya, ”Elimination of specular reflection and identification of ROI: The first step in automated detection of Cervical Cancer using Digital Colposcopy”, IEEE International Conference on Imaging Systems and Techniques (IST), pp.237–241, 2011.
[7]
P. Hannequin and J. Mas, “Statistical and heuristic image noise extraction (SHINE): a new method for processing Poisson Noise in Scintigraphic Images,” Phys. In Med. & Biol., Vol.47, pp.4329–4344, 2002.
[8]
J.van de Weijer, T. Gevers, J. M Geusebroek, “Edge and Corner Detection by Photometric Quasi-invariants”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.27, No.4, pp.625-630, 2005.
[9]
D. Nauck, R. Kruse, “A Fuzzy Neural Network Learning Fuzzy Control Rules and Membership Functions by Fuzzy Error Back propagation”, IEEE International Conference on Neural Networks, vol.2, pp.1022-1027, 1993.
[10]
Li Xin Wang, “A Course in Fuzzy Systems and Control”. Prentice-Hall Edition, chapter 13, pp.168-172., 1997.
[11]
M.F.Redondo, C.H.Espinosa, “A Comparison Among Feature Selection Method Based on Trained Network”, Neural Networks for Signal Processing IX Proceedings of the IEEE Signal Processing Society Workshop, pp.205-214, 1999.
[12]
S. Haykin, “Neural Networks, A Comprehensive Foundation”, Second edition, Prentice-Hall Edition, chapter 5, 1999.
[13]
S. Haykin, “Neural Networks, A Comprehensive Foundation”, Second edition, Prentice-Hall Edition, chapter 3, 1999