  In the last decade, researchers working in the domain of computer vision and
Artificial Intelligence (AI) have beefed up their efforts to come up with the
automated framework that not only detects but also identifies stage of breast
cancer. The reason for this surge in research activities in this direction are
mainly due to advent of robust AI algorithms (deep learning), availability of
hardware that can train those robust and complex AI algorithms and
accessibility of large enough dataset required for training AI algorithms.
Different imaging modalities that have been exploited by researchers to
automate the task of breast cancer detection are mammograms, ultrasound,
magnetic resonance imaging, histopathological images or any combination of
them. This article analyzes these imaging modalities and presents their
strengths, limitations and enlists resources from where their datasets can be
accessed for research purpose. This article then summarizes AI and computer
vision based state-of-the-art methods proposed in the last decade, to detect
breast cancer using various imaging modalities. Generally, in this article we
have focused on to review frameworks that have reported results using
mammograms as it is most widely used breast imaging modality that serves as
first test that medical practitioners usually prescribe for the detection of
breast cancer. Second reason of focusing on mammogram imaging modalities is the
availability of its labeled datasets. Datasets availability is one of the most
important aspect for the development of AI based frameworks as such algorithms
are data hungry and generally quality of dataset affects performance of AI
based algorithms. In a nutshell, this research article will act as a primary
resource for the research community working in the field of automated breast
imaging analysis.
