Evolving neural networks for the classification of malignancy associated changes

Malignancy Associated Changes are subtle changes to the nuclear texture of visually normal cells in the vicinity of a cancerous or precancerous lesion. We describe a classifier for the detection of MACs in digital images of cervical cells using artificial neural networks evolved in conjunction with an image texture feature subset. ROC curve analysis is used to compare the classification accuracy of the evolved classifier with that of standard linear discriminant analysis over the full range of classification thresholds as well as at selected optimal operating points. The nonlinear classifier does not significantly outperform the linear one, but it generalizes more readily to unseen data, and its stochastic nature provides insights into the information content of the data.