Breast cancer is one of the deadliest cancers in world. It essential to detect signs as early possible, make survival rate higher. However, detecting breast using machine or deep learning algorithms from diagnostic imaging results not trivial. Slight changes illumination scanned area can significantly affect automatic classification process. Hence, research aims propose an classifier for digita...

In this work, gray-scale invariant ranklet texture features are proposed for false positive reduction (FPR) in computer-aided detection (CAD) of breast masses. Two main considerations are at the basis of this proposal. First, false positive (FP) marks surviving our previous CAD system seem to be characterized by specific texture properties that can be used to discriminate them from masses. Seco...

We propose a method for segmentation and classification of breast cancer in digital mammograms using Independent Component Analysis (ICA), Texture Features and Multilayer Perceptron (MLP) Neural Networks. The method was tested for a mammogram set from MIAS database, resulting in 90.15% success rate, with 92% of specificity and 88.3% of sensitivity.