abstract

• —Infrared thermography can be used for pre-screening of breast cancer but the results of this technique depend on the experience of the human expert. We propose an automated analysis approach to assess the capabilities of deep neural networks to classify breast thermograms. The dataset consisted of 173 images and we compared seven deep learning architectures. VGG-16 convolutional neural network outperformed with a sensitivity of 100%25, specificity of 82.35%25 and balanced accuracy of 91.18%25. Such results indicate that deep neural networks can be used in the analysis of thermal images for breast cancer pre-screening. © 2019 IEEE.

authors

• González Contreras Francisco Javier
• Guevara Codina Edgar
• Torres-Galván J.C.

publication date

• 2019-01-01

funding provided via

• Consejo Nacional de Ciencia y Tecnología, CONACYT: 528  Grant

published in

• 2019 Photonics North, PN 2019  Proceedings

keywords

• Breast Cancer; Deep Learning; Thermography Deep learning; Diseases; Medical imaging; Network architecture; Neural networks; Temperature measuring instruments; Thermography (imaging); Thermography (temperature measurement); Automated analysis; Breast Cancer; Convolutional neural network; Human expert; Learning architectures; Thermal images; Deep neural networks

Digital Object Identifier (DOI)

• 10.1109/PN.2019.8819587

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