Nature-inspired Intelligent Techniques for Pap Smear Diagnosis: Ant Colony Optimization for Cell Classification

During the last years, Nature Inspired Intelligent Techniques have been very attractive. In this paper, one of the most important Nature Inspired Intelligent Techniques, the Ant Colony Optimization (ACO), is presented for the solution of the Pap Smear Cell Classification problem. ACO is derived from the foraging behaviour of real ants in nature. The main idea of ACO is to model the problem as the search for a minimum cost path in a graph. Artificial ants walk through this graph, looking for good paths. Each ant has a rather simple behaviour so that it will typically only find rather poor-quality paths on its own. Better paths are found as the emergent result of the global cooperation among ants in the colony. This algorithm is combined with a number of nearest neighbor based classifiers. The algorithm 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 cyto-technicians and doctors. Each cell is described by 20 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.