Identification of masses in digital mammograms with MLP and RBF nets

We study the identification of masses in digital mammograms using texture analysis. A number of texture measures are calculated for bilateral difference images showing regions of interest. The measurements are made on co-occurrence matrices in four different direction giving a total of seventy features. These features include the ones proposed by Haralick et al. (1973) and Chan et al. (1997). We study a total of 144 breast images from the MIAS database. The dimensionality of the dataset is reduced using principal components analysis (PCA), PCA components are classified using both multilayer perceptron networks using backpropagation (MLP) and radial basis functions based on Gaussian kernels (RBF). The two methods are compared on the same data across a ten fold cross-validation. The results are generated on the average recognition rate over these folds on correctly recognising masses and normal regions. Further analysis is based on the receiver operating characteristic (ROC) plots. The best results show recognition rates of 77% correct recognition and an area under the ROC curve value Az of 0.74.