Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
723374 | IFAC Proceedings Volumes | 2006 | 6 Pages |
Abstract
This work presents a multilayer perceptron (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.
Keywords
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
André Victor Alvarenga, Wagner C.A. Pereira, Antonio Fernando C. Infantosi, Carolina M. de Azevedo,