WBCD breast cancer database classification applying artificial metaplasticity neural network

The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. These techniques can help doctors form a second opinion and make a better diagnosis. In this paper we present a novel improvement in neural network training for pattern classification. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon’s information theory. During the training phase the Artificial metaplasticity Multilayer Perceptron (AMMLP) algorithm gives priority to updating the weights for the less frequent activations over the more frequent ones. In this way metaplasticity is modeled artificially. AMMLP achieves a more effcient training, while maintaining MLP performance. To test the proposed algorithm we used the Wisconsin Breast Cancer Database (WBCD). AMMLP performance is tested using classification accuracy, sensitivity and specificity analysis, and confusion matrix. The obtained AMMLP classification accuracy of 99.26%, a very promising result compared to the Backpropagation Algorithm (BPA) and recent classification techniques applied to the same database.

► The correct diagnosis of breast cancer is one of the major problems in the medical field. There are different pattern recognition techniques. We present a novel improvement in neural network training for pattern classification based by the biological metaplasticity. The performance Artificial metaplasticity Multilayer Perceptron (AMMLP) algorithm was tested using classification accuracy, sensitivity and specificity analysis, and confusion matrix. The result obtained in the classification accuracy by AMMLP was of 99.26%, a very promising result compared with recent classification techniques applied to the same database.