The Deep Neural Networks have gained prominence in the biomedical domain, becoming
the most commonly used networks after machine learning technology. Mammograms can be
used to detect breast cancers with high precision with the help of Convolutional Neural Network
(CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN
from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which
comparatively needs lesser training data during a mammogram screening. In the proposed study, the
application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered
microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of
breast tumor, extraction of features and augmentation of the image during mammogram classification
have been extensively reviewed.
Keywords: General adversarial networks, breast density estimation, microcalcification, breast tumour segmentation, feature
extraction, mammogram augmentation.
Rights & PermissionsPrintExport