GA-BACKPROPAGATION HYBRID TRAINING AND MORPHOMETRIC PARAMETERS TO CLASSIFY BREAST TUMOURS ON ULTRASOUND IMAGES

摘要

This work presents a multilayer perception (MLP) network, trained with backpropagation algorithm, to classify breast tumours as malign or benign ones. Seven morphometric parameters, extracted from the convex polygon and the normalised radial length techniques, are used as MLP input. A genetic-based selection procedure helps backpropagation training scheme to select the best input parameters and best training set, as well. To achieve this aim, an objective function is proposed. The best values of accuracy (97.4%), sensitivity (98.0%) and specificity (96.2%) were achieved with a set of five parameters, despite the training set sizes tested: 30% and 50% of the total samples.