Comparing The Performance Of MLP With One Hidden Layer And MLP With Two Hidden Layers On Mammography Mass Dataset

Abstract: Nowadays soft computing techniques such as fuzzy logic, artificial neural network and neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. In this paper, a multilayer perceptron neural network classifier is introduced to classify the mammography mass data set into two classes benign and malignant on the basis of mammography mass data set attributes. The performance of the MLP neural network in two different configurations is measured. In first configuration one hidden layer is used and in second two hidden layers are used. A four ?fold cross validation method is used for the assessment of generalization of the system. The result shows that the proposed MLP with two hidden layer achieve the accuracy of 89% approx., proving its usefulness in classification of mammography masses. Keywords: Mammography, Multilayer perceptron network, Cascade learning, four - fold cross validation.