Nature inspired intelligence in medicine: Ant colony optimization for pap-smear diagnosisNature inspired intelligence in medicine: Ant colony optimization for pap-smear diagnosis Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-052008enDuring the last years nature inspired intelligent techniques have become attractive for analyzing large data sets and solving complex optimization problems. In this paper, one of the most interesting of them, the Ant Colony Optimization (ACO), is used for the construction of a hybrid algorithmic scheme which effectively handles the Pap Smear Cell classification problem. This algorithmic approach is properly combined with a number of nearest neighbor based approaches for performing the requested classification task, through the solution of the so-called optimal feature subset selection problem. The proposed complete algorithmic scheme is tested in two sets of data. The first one consists of 917 images of pap smear cells and the second set consists of 500 images, classified carefully by expert cyto-technicians and doctors. Each cell is described by 20 numerical features, and the cells fall into seven (7) classes, four (4) representing normal cells and three (3) abnormal cases. Nevertheless, from the medical diagnosis viewpoint, a minimum requirement corresponds to the general two-class problem of correct separation between normal from abnormal cells. http://creativecommons.org/licenses/by/4.0/International Journal on Artificial Intelligence Tools217279-301 Marinakis Ioannis Μαρινακης Ιωαννης Dounias, G Pap smear Papanicolaou smear Papanicolaou test pap test pap smear papanicolaou smear papanicolaou test