Feature Extraction and Feature Set Selection for Cervical Cancer Diagnosis

Abstract

Objectives: To identify a cervical cytology cell image as a representative of the normal or malignant cancerous cell. This work may be used to decide the stage of cervical cancer. Methods and Analysis: Images from the Hervlev university database have been used. Features of nucleus and cytoplasm of cervical cell images are extracted after preprocessing and segmentation. Size, shape and texture features are extracted. Appropriate and adequate features are selected by mining techniques. This feature set is fed to classifiers to decide nature of cells. Based on parameters like true positive rate, true negative rate and precision, a comparative analysis of classifiers is done. The best classifier that clearly discriminates the cells is determined for cervical cancer diagnosis. Findings: Dimensionality reduction of feature set can be done with data mining technique named as Correlation based feature subset selection. Reduced feature set can be used for classification by classifiers like Multilayer perceptron, Bayes classifiers and SVM classifier. Kappa Statistics for binary classifiers is 1 for Multilayer perceptron, 0.9968 for Binary SMO and Bayesnet, 0.9915 for NaiveBayes Classifiers. The same for seven group classifiers is 1 for LibSVM, 0.8535 for Multilayer perceptron, 0.8464 for attribute selected classifier and 0.7286 for Bagging classifier. True positive rate is 1 for Multilayer perceptron, 0.996 for Binary SMO and BayesNet, 0.975 for NaiveBayes classifier. Multilayer perceptron can be used as a classifier in both binary and seven class classifications. Improvement: Existing methods use more features for classification. Here even in the case of seven class classifier, only 11 features are used and desired result has been achieved.