Fully automated methods and systems for processing complex data sets to identify abnormalities are described. In one embodiment, the system includes wavelet processing, recursive processing to determine prominent features, and then utilizing feed forward neural networks (FFNNs) to classify feature vectors generated in the wavelet and recursive processing. With respect to wavelet processing, multiresolution (five-level) and multidirection (two-dimensional) wavelet analysis with quadratic spline wavelets is performed to transform each image. The wavelets are a first-order derivative of a smoothing function and enhance the edges of image objects. Because two-dimensional wavelet transforms quantize an image in terms of space and spatial frequency and can be ordered linearly, the data is processed recursively to determine prominent features. A neural network approach derived from sequential recursive auto-associative memory is then used to parse the wavelet coefficients and hierarchy data. Since the wavelet coefficients are continuous, linear output instead of sigmoidal output is used. This variation is therefore referred to as linear output sequential recursive auto-associative memory, or LOSRAAM. The objective of training the LOSRAAM network is to have the output exactly match the input. Context units arising from serial evaluation of the wavelet coefficient triplets may be collected as vectors. These vectors are subjected to cluster analysis. This analysis yields a number of identifiable and discrete states. From these states, feature vectors are created. Each element in the feature vector represents the number of times the corresponding state from the above cluster analysis is found. Then, feed forward neural networks (FFNNs) are trained to classify feature vectors.