Particle swarm optimization for pap-smear diagnosisParticle swarm optimization for pap-smear diagnosis Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-052008enThe 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.http://creativecommons.org/licenses/by/4.0/Expert Systems with Applications435 1645–1656 Marinakis Ioannis Μαρινακης Ιωαννης Marinaki Magdalini Μαρινακη Μαγδαληνη Dounias, G Elsevier Pap smear Papanicolaou smear Papanicolaou test pap test pap smear papanicolaou smear papanicolaou test