A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images

Abstract Breast cancer is now widely known to be the second most lethal disease among women. Computer-aided detection (CAD) systems, deep learning (DL) in particular, have continued to provide significant computational solution in early detection and diagnosis of this disease. Research efforts are advancing novel approaches to improve the performance of DL-based models. Techniques such as data augmentation, varying depth of model, image quality enhancement, and choice of classifier have been proposed to improve performance in the characterization of abnormalities in mammograms. However, no significant progress has been made in applying deep learning techniques to the detection of architectural distortion – a form of abnormalities in breast images. In this research, we propose a novel convolution neural network (CNN) model for the detection of architectural distortion by enhancing its performance using data augmentation technique. We also investigate the performance of the proposed model on different operations of image augmentation. Furthermore, the new model was adapted to detect images presenting the right and left breast presented in MLO and CC views. Similarly, we investigate the performance of our model under the fixed-size region of interests (ROIs) and multi-size whole images inputs. Our method was trained on 5136 ROIs from MIAS, 410 whole images from INbreast, 322 whole images from MIAS, and 55,890 ROIs from DDSM + CBS databases. Performance evaluation of the proposed model in comparison with other state-of-the-art techniques revealed that the model achieved 93.75 % accuracy. This study has, therefore, strengthened the need to leverage data augmentation techniques to enhance the detection of architectural distortion, thereby reducing the rate of advanced cases of breast cancer.