High breast density (BD) is recognized as an independent risk factor for breast cancer development, in addition to negatively impacting the sensitivity of mammography by hiding tumor masses. Although BD is normally assessed with the BI-RADS reporting system, this evaluation is qualitative and has been shown to vary considerably across readers, which usually divide density into four different classes. In this study, it’s presented a deep learning (DL) method to quantify BD from a standard two-view (cranio-caudal, and medio-lateral-oblique) mammography exam. With the aim of developing a method based on an objective ground truth, the DL model was trained and validated using 88 simulated mammograms from an equal number of distinct 3D digital breast phantoms for which BD is known.