Paper Info
Title
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.
Year
DOI
Venue
2016
10.1007/978-3-319-51281-5_39
RECENT ADVANCES ON SOFT COMPUTING AND DATA MINING
Keywords
Field
DocType
Nonparametric classifier,Malignancy associated changes,Cancer screening
Cervical cancer,Receiver operating characteristic,Computer science,Pooling,Nonparametric statistics,Artificial intelligence,Statistical classification,Classifier (linguistics),Cancer screening,Machine learning
Conference
Volume
ISSN
Citations 
549
2194-5357
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Noor Azah Samsudin1154.54
Aida Mustapha29026.18
Nureize Arbaiy3259.78
Isredza Rahmi A. Hamid4323.75