Breast cancer is the uncontrolled growth of abnormal cells that start in the breast. It was and still the most common cancer diagnosed in women worldwide. In this book, Computer-aided detection (CADe) and Computer-aided diagnosis (CADx) systems are developed and applied to the standard MIAS and DDSM databases to distinguish between the different breast tissues. In both CAD systems, regions of interest (ROIs) are selected then a set of 422 features are extracted and normalized. The features selection is performed using statistical methods such as t-test, KS-test and W-test and other methods by using Matlab PR-Toolbox such as SBS, SFS, SFFS and BBS techniques. Then four classifiers used including KNN, SVM, LDA and QDA for classification stage. The proposed systems were evaluated using many indices such as Overall accuracy, Cohen-k factor, Sensitivity, Specificity, PPV, NPV and the Area Under Curve (AUC) of the ROC curves. CADx has the same stages but the output is different where it indicates the likelihood of lesion malignancy. Both CAD systems provided encouraging results. These results were different corresponding to the selection method.