A Survey of Breast Cancer Screening Techniques: Thermography and Electrical Impedance Tomography

TL;DR

This survey reviews recent advances in thermography, electrical impedance tomography, and computational methods for breast cancer screening, highlighting their potential as faster, cheaper, and reliable alternatives or complements to mammography.

Contribution

It provides a comprehensive overview of recent technological and computational developments in alternative breast cancer screening techniques and compares various machine learning approaches for diagnosis.

Findings

Thermography and electrical impedance tomography reduce false positives and negatives.

Machine learning models improve diagnostic accuracy.

Combining techniques enhances global performance.

Abstract

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.