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Abstract
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Breast cancer is the most common kind of cancer, which is the cause of death among the women
worldwide. There is evidence that shows that the early detection and treatment can increase the
survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic
breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and
parameter optimization of an Multi Layer Perceptron (MLP) neural network. The aim of this paper
is to propose a hybrid classification algorithm based on Multi-stage Weights Adjustment in the
MLP (MWAMLP) neural network in two parts to improve the breast cancer diagnosis. In the first
part, the three classifiers are trained simultaneously on the learning dataset. The output of the first
part classifier together with the learning dataset is placed in a new dataset. This dataset uses a
hybrid classifier method to model the mapping between the outputs of each ordinary classifier of
the first part with real output labels. The proposed algorithm is implemented with three different
variations of the backpropagation (BP) technique, namely the Levenberg–Marquardt, resilient BP
and gradient descent with momentum for fine tuning of the weight of MLP neural network and
their performances are compared. Interestingly, one of the proposed algorithms titled MWAMLPRP
produces the best and on average, 99.35% and 98.74% correct classification, respectively, on the
Wisconsin Breast Cancer Database dataset, which is comparable with the obtained results from the
methods titled GP-DLNN, GAANN and CAFS and other works found in the literature.
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