Grouped mask region convolution neural networks for improved breast cancer segmentation in mammography images

Mammography is one of the most effective tools radiologists use to detect breast cancer early, as it can detect cancer up to ten years before it manifests. The accuracy of breast cancer segmentation using computer aided design (CAD) systems is challenged by several factors, such as insufficient data for model training and data complexity. For example, mammography images are symmetrical by nature and appear complex to computer vision models, making accurate segmentation difficult. Data augmentation techniques are employed by researchers to help computer vision models preserve the invariance and equivariance features of transformed input images. However, these techniques have been insufficient and incur a computational cost. To improve the data efficiency of the computer vision models, this study proposes a model that combines the functionalities of the Mask Region Convolutional Neural Networks (Mask-RCNN) and Group Convolutional Neural Networks (GCNN) to provide highly accurate breast cancer segmentation. The GCNN preserves the invariant rotation of the transformed mammography images, increases weight sharing and the expressive capacity of the model, and minimizes computational costs. Our proposed model was applied to two mammography datasets, INbreast and MIAS. It achieved competitive results in terms of accuracy, Dice coefficient, Jaccard index, specificity, and sensitivity of 99.01%, 86.63%, 87.76%, 99.24%, and 98.55%, respectively, compared to conventional architectures.