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In this paper we present a high accuracy computer-aided diagnosis scheme. The goal of the developed system is to classify benign and malignant microcalcifications on mammograms. It is mainly based on a combination of wavelet decomposition, feature extraction and classification methodology using Fisherpsilas linear discriminant. The contribution of wavelet decomposition is to denoise and to enhance regions of interests (ROI) containing abnormalities. Feature extraction is performed using spatial grey level dependence (SGLD) matrices. The purpose of classification is to assign an object to a certain class. Many classification methods have been described. Here we use Fisher's linear discriminant. Fisherpsilas linear discriminant is particularly useful for discriminating between two classes in a multidimensional space. Since it is based only on the first and second moments of each distribution, it is not a computationally intensive method. Our results show that the developed method is effective for quantifying the classification of benign and malignant microcalcifications abnormalities with an accuracy of 95.5%.