Comparative Analysis of Convolutional Neural Networks for Classification of Breast Abnormalities

Abstract—Computer-aided detection (CAD) systems are used by radiologists as a second interpreter for breast cancer detection in digital mammography. However, for every true-positive cancer detected by the CAD system, a large number of false predictions must be reviewed by an expert to avoid an unnecessary biopsy. The traditional approach to creating such systems is to select and compute the features of objects of interest from the source data, followed by the selection of a model for their classification using machine learning. Machine learning and, especially, deep learning are being used to analyze mammograms. Most of the models proposed so far are trained on a small amount of data and do not have high reliability. This paper compares several deep learning models for benign–malign mammography classification on digital mammograms. The preprocessing step is designed to remove noise and extract features using local phase information of the image. Deep learning is then used to classify the digital mammography. The experimental results are presented using several databases and estimated using several quality criteria.