Multi-scale CNN based on region proposals for efficient breast abnormality recognition

Mammographic pattern recognition is one of the most essential tasks in breast cancer diagnosis, and has been studied for several years now to make it suitable and faster. In this paper, we developed a novel deep Convolutional Neural Network (CNN) approach to discriminate normal from abnormal breast tissues using Gaussian pyramid representation for multi-scale analysis (Pyramid-CNN). In order to improve image processing time, we extracted representative region proposals from each mammogram using determinant of the Hessian operator. To improve performance of our model and avoid overfitting, data augmentation techniques based on geometric transformation and sub-histogram equalization were applied on all regions to increase the variance of significant mammographic samples. We evaluated our methodology on the publicly available mammography dataset such as Breast Cancer Digital Repository (BCDR) database. In comparison with the current state-of-the-art methods, the experiments show that our proposed system provides efficient results, achieving the average accuracy of 96.84%, sensitivity of 92.12%, specificity of 98.02%, precision of 92.15%, F1-score of 92.12%, and area under the receiver operating characteristic curve (AUC) of 96.76%. Hence, the study demonstrates that our proposed approach has the potential to significantly improve the conventional recognition and classification strategies for use in advanced clinical application and practice or in general, biomedical imaging field.