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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 264246, 10 pages
Research Article

Evaluation of the Diagnostic Power of Thermography in Breast Cancer Using Bayesian Network Classifiers

1Departamento de Inteligencia Artificial, Universidad Veracruzana, Sebastián Camacho 5, Centro, 91000 Xalapa, VZ, Mexico
2Centro Estatal de Cancerología: Miguel Dorantes Mesa, Aguascalientes 100, Progreso Macuiltepetl, 91130 Xalapa, VZ, Mexico
3Laboratorio Nacional de Informática Avanzada (LANIA) A.C. Rébsamen 80, Centro, 91000 Xalapa, VZ, Mexico

Received 25 October 2012; Revised 4 April 2013; Accepted 22 April 2013

Academic Editor: Alejandro Rodríguez González

Copyright © 2013 Cruz-Ramírez Nicandro et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool.