Automated identification of cancerous smears using various competitive intelligent techniques

In this study the performance of various intelligent methodologies is compared in the task of pap-smear diagnosis. The selected intelligent methodologies are briefly described and explained, and then, the acquired results are presented and discussed for their comprehensibility and usefulness to medical staff, either for fault diagnosis tasks, or for the construction of automated computer-assisted classification of smears. The intelligent methodologies used for the construction of pap-smear classifiers, are different clustering approaches, feature selection, neuro-fuzzy systems, inductive machine learning, genetic programming, and second order neural networks. Acquired results reveal the power of most intelligent techniques to obtain high quality solutions in this difficult problem of medical diagnosis. Some of the methods obtain almost perfect diagnostic accuracy in test data, but the outcome lacks comprehensibility. On the other hand, results scoring high in terms of comprehensibility are acquired from some methods, but with the drawback of achieving lower diagnostic accuracy. The experimental data used in this study were collected at a previous stage, for the purpose of combining intelligent diagnostic methodologies with other existing computer imaging technologies towards the construction of an automated smear cell classification device.