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Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification

Marinakis Ioannis, Georgios Dounias, Jan Jantzen

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URIhttp://purl.tuc.gr/dl/dias/6DEB5095-8D1D-4F2F-9112-E6CAC3B7B01A-
Identifierhttps://doi.org/10.1016/j.compbiomed.2008.11.006-
Languageen-
TitlePap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classificationen
CreatorMarinakis Ioannisen
CreatorΜαρινακης Ιωαννηςel
Creator Georgios Douniasen
CreatorJan Jantzenen
PublisherElsevieren
Content SummaryThe 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 MDs, consisting of 917 and 500 images of pap smear cells, respectively. Each cell is described by 20 numerical features, and the cells fall into 7 classes but a minimal requirement is to separate normal from abnormal cells, which is a 2 class problem. For finding the best possible performing feature subset selection problem, an effective genetic algorithm 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.en
Type of ItemΠερίληψη Δημοσίευσης σε Συνέδριοel
Type of ItemConference Paper Abstracten
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-30-
Date of Publication2006-
Bibliographic CitationY. Marinakis and G. Dounias, “Pap Smear Diagnosis Using a Hybrid Intelligent Scheme Focusing on Genetic Algorithm Based Feature Selection and Nearest Neighbor Classification”, in The Pap Smear Benchmark, Intelligent and Nature Inspired Approaches in Pap Smear Diagnosis, Special Session Proceedings of the NISIS - 2006 Symposium, 2006, pp. 69–78. doi: 10.1016/j.compbiomed.2008.11.006en

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