  Breast cancer is a disease that threatens many women's life, thus, early and
accurate detection plays a key role in reducing the mortality rate. Mammography
stands as the reference technique for breast cancer screening; nevertheless,
many countries still lack access to mammograms due to economic, social, and
cultural issues. Last advances in computational tools, infrared cameras, and
devices for bio-impedance quantification allowed the development of parallel
techniques like thermography, infrared imaging, and electrical impedance
tomography, these being faster, reliable and cheaper. In the last decades,
these have been considered as complement procedures for breast cancer
diagnosis, where many studies concluded that false positive and false negative
rates are greatly reduced. This work aims to review the last breakthroughs
about the three above-mentioned techniques describing the benefits of mixing
several computational skills to obtain a better global performance. In
addition, we provide a comparison between several machine learning techniques
applied to breast cancer diagnosis going from logistic regression, decision
trees, and random forest to artificial, deep, and convolutional neural
networks. Finally, it is mentioned several recommendations for 3D breast
simulations, pre-processing techniques, biomedical devices in the research
field, prediction of tumor location and size.
