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Intelligent and nature inspired optimization methods in medicine: the Pap smear cell classification problem
Author(s) -
Marinakis Yannis,
Marinaki Magdalene,
Dounias Georgios,
Jantzen Jan,
Bjerregaard Beth
Publication year - 2009
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2009.00506.x
Subject(s) - computer science , artificial intelligence , ant colony optimization algorithms , metaheuristic , set (abstract data type) , particle swarm optimization , optimization problem , pattern recognition (psychology) , genetic algorithm , feature selection , noise (video) , machine learning , algorithm , image (mathematics) , programming language
The classification problem consists of using some known objects, usually described by a large vector of features, to induce a model that classifies others into known classes. Feature selection is widely used as the first stage of the classification task to reduce the dimension of the problem, decrease noise and improve speed by the elimination of irrelevant or redundant features. The present paper deals with the optimization of nearest neighbour classifiers via intelligent and nature inspired algorithms for a very significant medical problem, the Pap smear cell classification problem. The algorithms used include tabu search, genetic algorithms, particle swarm optimization and ant colony optimization. The proposed complete algorithmic scheme is tested on two sets of data. The first 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 classes representing a variety of normal and abnormal cases. Nevertheless, from the medical diagnosis viewpoint, a minimum requirement corresponds to the general two‐class problem of correct separation between normal and abnormal cells.