Evolutionary Neural Networks Applied To The Classification Of Microcalcification Clusters In Digital Mammograms

Breast cancer is one of the main causes of death in women and early diagnosis is an important means to reduce the mortality rate. The presence of microcalcification clusters are primary indicators of early stages of malignant types of breast cancer and its detection is important to prevent the disease. This paper proposes a procedure for the classification of microcalcification clusters in mammograms using sequential difference of Gaussian filters (DoG) and three evolutionary artificial neural networks (EANNs) compared against a feedforward neural network (NN) trained with backpropagation. We found that the use of genetic algorithms (GAs) for 1) finding the optimal weight set for a NN, 2) finding an adequate initial weight set before starting a backpropagation training algorithm and 3) designing its architecture and tuning its parameters, results mainly in improvements in overall accuracy, sensitivity and specificity of a NN, compared with other networks trained with simple backpropagation.