Breast Density Classification to Aid Clinical Workflow in Breast Cancer Detection Using Deep Learning Network

Breast density is an important bio-marker for predicting the risk of breast dancer. Studies have showed that women with dense breast have higher probability of breast cancer. The density, assessed using mammogram images, is a measure of amount of fibro-glandular tissues in a breast and the appearance of it can mask the lesions leading to lesser sensitivity in breast cancer detection. Hence the current clinical workflow for breast detection incorporates a density classification stage which suggest detailed analysis of dense breast using additional imaging modalities like Digital Breast Tomosynthesis (DBT). In this work a breast density classifier using deep neural network architecture employing transfer learning is proposed for the binary classification of breast density to assist in the clinical workflows. The results are evaluated on publicly available databases namely DDSM, MIAS and InBreast. The proposed model outperforms the existing works in the literature giving on an average 96 % and 94 % accuracy respectively, when tested on DDSM and MIAS database.