Automatic breast mass detection in mammograms using density of wavelet coefficients and a patch-based CNN.

Purpose: For the purpose of accurate and efficient mass detection in full-field digital mammograms, we propose a method for automated mass detection that consists of two stages: suspicious region localization and false-positive (FP) reduction, by classifying these regions into mass and non-mass regions (normal tissues).

Methods: In the first stage, the density of the wavelet coefficients based on Quincunx Lifting Scheme (DWC-QLS) is used to find suspicious regions (regions of interest, ROIs) in full mammograms. In the second stage, a patch-based CNN classifier is developed as an FP reduction to classify the suspicious regions.