An Intensive Analysis of Deep Learning Impacts on Breast Cancer Diagnosis and Practices

Abstract

Medical imaging allows for the visualization and mathematical modeling of biological processes that are critical for the screening mammography. Since it is limited by meritocratic abilities of high descriptive capability in visual assessment and traditional machine learning approaches, deep learning has been widely used in medical imaging research in recent years. Deep learning, as a broader model, demands less computational technology and enables for more accurate data volumes in forecasting. We present a study that uses deep learning to investigate aspects of breast cancer detection and diagnosis. Second, we present a mammography-specific deep learning model. Finally, we include an overview and input on recent work on deep learning systems for breast cancer detection and diagnosis, as well as some potential open issues.