Particle swarm optimization for pap-smear diagnosis

Authors:
Yannis Marinakis;Magdalene Marinaki;Georgios Dounias
Affiliations:
Decision Support Systems Laboratory, Department of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece;Industrial Systems Control Laboratory, Department of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece;Department of Financial and Management Engineering, Management and Decision Engineering Laboratory, University of the Aegean, 31 Fostini Str., 82100 Chios, Greece
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

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.