Evolution of neural networks for the detection of breast cancer

Abstract

This paper is based on a modified form of Fogel's evolutionary programming approach for evolving neural networks for the detection of breast cancer using fine needle aspirate data. A data visualization and preprocessing description is given, which not only depicts the benign and malignant raw data in graphical interpretative form but also includes a "symmetrized dot pattern" of this same data which may be used to corroborate the classification provided by the network. These evolved architectures routinely achieved a greater than 96% classification accuracy while, at the same time, achieving a much smaller type II error (calling a malignant sample benign). These results were obtained with different data sets using the same architecture, and were also obtained with the same data set over a family of evolved architectures.