An intelligent Computer-Aided Detection system (CAD) can be very helpful indetecting masses in the breast earlier and faster than typical screening programs. Two such systems are presented, first a system based on Radial Basis neural networks coupled with feature extraction techniques for detecting masses in digital mammograms. Suspicious regions are identified following a run of the trained neural network. Five co-occurrence matrices are constructed at different distances for each mammogram. A number of statistical features are used to train and test the Radial Basis neural network. The second system presented was developed based on linear subtraction and feature extraction techniques to identify asymmetries between left and right breast mammograms. This system is based on the idea that a deviation from the normal architectural symmetry of the right and left breasts could indicate a cancerous mass. The mammograms were first normalized, registered, and then linear subtraction was followed by various feature extraction techniques that were used in order to reduce the number of false positive detections. Computerized detection was evaluated by using Receiver Operating Characteristic analysis (ROC), which measured the overall sensitivity of the presented systems by the area under the curve Az. The results show that both systems could be helpful to the radiologist by serving as a second reader in mammography screening.