  Breast cancer is one of the leading fatal disease worldwide with high risk
control if early discovered. Conventional method for breast screening is x-ray
mammography, which is known to be challenging for early detection of cancer
lesions. The dense breast structure produced due to the compression process
during imaging lead to difficulties to recognize small size abnormalities.
Also, inter- and intra-variations of breast tissues lead to significant
difficulties to achieve high diagnosis accuracy using hand-crafted features.
Deep learning is an emerging machine learning technology that requires a
relatively high computation power. Yet, it proved to be very effective in
several difficult tasks that requires decision making at the level of human
intelligence. In this paper, we develop a new network architecture inspired by
the U-net structure that can be used for effective and early detection of
breast cancer. Results indicate a high rate of sensitivity and specificity that
indicate potential usefulness of the proposed approach in clinical use.
