期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 35, 期 4, 页码 1645-1656出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2007.08.089
关键词
particle swarm optimization; feature selection problem; pap-smear classification; nearest neighbor based classifiers
The term pap-smear refers to samples of human cells stained by the so-called Papanicolaou method. The purpose of the Papanicolaou method is to diagnose pre-cancerous cell changes before they progress to invasive carcinoma. In this paper, a metaheuristic algorithm is proposed in order to classify the cells. Two databases are used, constructed in different times by expert Medical Doctors, consisting of 917 and 500 images of pap-smear cells, respectively. Each cell is described by 20 numerical features and the cells fall into seven classes but a minimal requirement is to separate normal from abnormal cells which is a two-class problem. For finding the best possible performing feature subset, an effective particle swarm optimization scheme is proposed. This algorithmic scheme is combined with a number of nearest neighbor based classifiers. Results show that classification accuracy generally outperforms other previously applied intelligent approaches. (C) 2007 Elsevier Ltd. All rights reserved.
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