Extended Local Mean-Based Nonparametric Classifier for Cervical Cancer Screening

Abstract

Malignancy associated changes approach is one of possible strategies to classify a Pap smear slide as positive (abnormal) or negative (normal) in cervical cancer screening procedure. The malignancy associated changes (MAC) approach acquires analysis of the cells as a group as the abnormal phenomenon cannot be detected at individual cell level. However, the existing classification algorithms are limited to automation of individual cell analysis task as in rare event approach. Therefore, in this paper we apply extended local-mean based nonparametric classifier to automate a group of cells analysis that is applicable in MAC approach. The proposed classifiers extend the existing local mean-based nonparametric techniques in two ways: voting and pooling schemes to label each patient’s Pap smear slide. The performances of the proposed classifiers are evaluated against existing local mean-based nonparametric classifier in terms of accuracy and area under receiver operating characteristic curve (AUC). The extended classifiers show favourable accuracy compared to the existing local mean-based nonparametric classifier in performing the Pap smear slide classification task.