![]() ![]() The algorithm is able to achieve 100% sensitivity with 2.59, 1.78, and 0.68 average false positives per image on Digital Database for Screening Mammography (scanned film), INbreast (direct radiography) database, and PGIMER-IITKGP mammogram (direct radiography) database, respectively. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. Several texture, shape, intensity, and histogram of oriented gradients (HOG)–based features are used to distinguish microcalcifications from other brighter mammogram regions. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. Up to 50% of non-palpable breast cancers are detected solely through microcalcification clusters in mammograms. Breast cancer is the most common cancer diagnosed in women worldwide.
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