Data Augmentation-aided Convolutional Neural Network for Detection of Abnormalities in Digital Mammography

Abstract

Background: The use of data augmentation techniques to addressing the challenge of network overfitting and classification error is important in deep learning. Insufficient sample data for training have the tendency to bias the trained model so that it fails to generalize well. Several studies have proposed different augmentation techniques to solve this problem. But there are some peculiarities identified with the nature of datasets when applying augmentation methods. The subtle nature of some abnormalities in digital mammography often makes it difficult to transform such datasets into different form, while preserving the structure of the abnormality. Aim: To address this, this study aims to apply a combination of carefully selected data augmentation operations on digital mammography.