Based on chemical composition, two types of microcalcifications are found within the breast. Type I calcifications consist of calcium oxalate (CaOx), whereas Type II calcifications are composed of calcium phosphates, mainly hydroxyapatite (CaHa). Previous tissue analysis studies1, 2 have shown that Type I calcifications are typically found in benign lesions, and are not associated with carcinoma. Type II calcifications, on the other hand, can be found in both benign and malignant breast lesions. It was estimated1 that lesions with solely Type I calcifications represented 12-17% of benign biopsies performed. Therefore, an imaging method that could differentiate calcified lesions with solely CaOx calcifications might help in reducing unnecessary breast biopsies. Previous studies3-5 have shown that dual-energy mammography can be an effective tool in discriminating between these two classes of calcifications. In this work we investigate if a convolutional neural network (CNN) can be trained to distinguish between these two types of calcified clusters, using dual-energy x-ray mammograms. We implement a Monte Carlo mammography simulator package to model a dual-energy FFDM system and use a realistic anthropomorphic digital breast phantom model6 with embedded microcalcifications. A Resnet CNN architecture was used to classify the ROIs containing either CaOX, or CaOx clusters. We report classification accuracy results in terms ROC curve and the corresponding area under it.