Role of General Adversarial Networks in Mammogram Analysis: A Review

Annapoorani       Gopal; Lathaselvi       Gandhimaruthian; Javid       Ali
Abstract

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.
Graphical Abstract

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Cite As
Current Medical Imaging

Role of General Adversarial Networks in Mammogram Analysis: A Review

Abstract

Graphical Abstract
