Classification of heart diseases in ultrasonic images using neural networks trained by genetic algorithms

Recent studies show the effectiveness of neural-network-based computer-aided-diagnosis schemes for automated detection of various diseases, such as malignant breast mass and lung nodules. In this paper we describe a method for automated classification of ultrasonic heart (echocardiographic) images. The feature of the method is to employ an artificial neural network (NN) trained by genetic algorithms (GA´s) instead of backpropagation. With the GA the optimal weighting coefficients of the NN are determined. Also the method shows a faster convergence for obtaining the optimal solution in NN training. Experiments on different data sets show the superiority of the GA-based method over backpropagation for classification