Breast cancer is a disease that threat many women's life, thus, the early and accurate detection play a key role in reducing the risk in patient's life. 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, have given the chance to emerge other reference techniques like, thermography, infrared thermography and electrical impedance tomography, these being faster, reliable and cheaper. In the last decades, these have been considered as parallel procedures for breast cancer diagnosis, as well many authors concluded that false positives and false negatives rates are greatly reduce. This work aims to review the last breakthroughs about the three above-mentioned techniques and to describe the benefits of mixing several computational skills to obtain a better global performance.In addition, we provide a comparison between several machine learning technique going from logistic regression, decision trees and random forest to artificial, deep and convolutional neural networks. Finally, recommendations and contemporary advances in breast cancer diagnosis approaches are made, such as 3D breast simulations, pre-processing techniques, devices in the research field, prediction of tumor location and size.

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