Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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| URI: | http://purl.tuc.gr/dl/dias/5F20CA3D-1C95-4A91-BEFA-C2F1EF4EA6CE | ||
| Έτος | 2008 | ||
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | ||
| Άδεια Χρήσης |
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| Βιβλιογραφική Αναφορά | Y. Marinakis, M. Marinaki , G. Dounias, "Particle swarm optimization for pap-smear diagnosis, "Expert Syst.with Appl. ,vol. 35,no.4, pp.1645-1656,Nov. 2008.doi:10.1016/j.eswa.2007.08.089 https://doi.org/10.1016/j.eswa.2007.08.089 | ||
| Εμφανίζεται στις Συλλογές |
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.
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