  Computer Aided Diagnosis (CAD) system has been developed for the early
detection of breast cancer, one of the most deadly cancer for women. The benign
of mammogram has different texture from malignant. There are fifty mammogram
images used in this work which are divided for training and testing. Therefore,
the selection of the right texture to determine the level of accuracy of CAD
system is important. The first and second order statistics are the texture
feature extraction methods which can be used on a mammogram. This work
classifies texture descriptor into nine groups where the extraction of features
is classified using backpropagation learning with two types of multi-layer
perceptron (MLP). The best texture descriptor as selected when the value of
regression 1 appears in both the MLP-1 and the MLP-2 with the number of epoches
less than 1000. The results of testing show that the best selected texture
descriptor is the second order (combination) using all direction (0, 45, 90 and
135) that have twenty four descriptors.
