Neural classification of abnormal tissue in digital mammography using statistical features of the texture

The authors investigated the efficiency of neural classifiers in recognizing cancer regions of suspicion (ROS) on mammograms. Radial-basis-function (RBF) networks and multilayer perceptron (MLP) neural networks are used to classify ROS including all kinds of abnormalities by processing two types of texture features: statistical descriptors based on high-order statistics and the spatial gray-level dependence (SGLD) matrix. Extensive experiments carried out in the MIAS database have given similar recognition scores for both types of features. The MLP classifier outperforms the score achieved by the RBF networks. Significantly greater training time and computational complexity both in the training and the classification process measured for the MLP networks. Specifically, the recognition accuracy of the MLP is approximately 4% better than that obtained by the RBF networks for the statistical descriptors based on high-order statistics. Using the SGLD matrix the RBF network exceeded the recognition rate of the MLP networks only in one case out of three.