Whole mammogram image classification with convolutional neural networks

Abstract

Due to the high variability in tumor morphology and the low signal-to-noise ratio inherent to mammography, manual classification of mammogram yields a significant number of patients being called back, and subsequent large number of biopsies performed to reduce the risk of missing cancer. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. This technique has achieved significant advancements in large-set image-classification challenges in recent years. In this study, we had obtained over 3000 high-quality original mammograms with approval from an institutional review board at the University of Kentucky. Different classifiers based on CNNs were built, and each classifier was evaluated based on its performance relative to truth values generated by histology results from biopsy and two-year negative mammogram follow-up confirmed by expert radiologists. Our results showed that CNN model we had built and optimized via data augmentation and transfer learning have a great potential for automatic breast cancer detection using mammograms.