A survey of breast cancer screening techniques: thermography and electrical impedance tomography

Abstract

Breast cancer is a disease that threat many women’s life, thus, the early and accurate detection play 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, infra-red cameras and devices for bio-impedance quantification allowed the development of parallel techniques like, thermography, infra-red 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 tumour location and size.

Keywords:

• Breast cancer
• thermography
• electrical impedance tomography
• machine learning techniques
• computer aided diagnosis

Notes

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Dense tissue: high index of fibrous or glandular tissue and low of fat.

2 AACR, American Association for Cancer Research.

3 (1) A graph indicating the pulsing of the blood within the vein. (2) An X-ray image of a vein that has been injected with a dye that is visible on the image taken, Collins Dictionary.

4 Meta-algorithms that combines several machine learning techniques into just one predictive model decreasing variance, bias and accuracy, the resulted model is better than the other ones separately.

5 In [62] is mention that 5% of the all articles from January 2012 to January 2017 regarding CAD techniques for diagnosis of breast cancer mention thermography as main method.

6 Tiny tumours cannot be detected with this method [51].

7 The journal of cancer research, AACR. Department of Biophysics, Cleveland Clinic Foundation, Ohio.

8 The inverse conductivity problem is the mathematical problem that must be solved in order for electrical impedance tomography systems to be able to make images [83].

9 A class of feedforward artificial neural network, at least is composed by 3 layers.

10 T-Scan 2000ED, from Mirabel Medical Systems, Austin, TX.

11 Study from: Fraser Health Authority and Jim Pattison Outpatient Care and Surgery Centre (JPOCSC) with study number FHREB2014-065 and 2015s0156, respectively.

Additional information

Funding

This study has been support by the INTERREG institution, section France and Switzerland under the framework of the SBRA project.