abstract

• Temperature patterns of the breast measured using infrared thermography have been used to detect changes in blood perfusion that can occur due to inflammation, angiogenesis, or other pathological causes. In this work, 94 thermograms of patients with suspected breast cancer were analyzed using an automatic classification method, based on a convolutional neural network. In particular, our approach uses a deep convolutional neural network (CNN) with transfer learning to automatically classify thermograms into two different tasks: normal and abnormal thermograms, and malign and benign lesions. Class Activation Mapping is used to show how the network can focus on the affected areas without having received this information. Several measurements were carried out to validate the performance of the network in each task and these results suggest that deep convolutional neural networks with transfer learning are able to detect thermal anomalies in thermograms with sensitivity similar to that of a human expert, even in cohorts with a low prevalence of breast cancer. © 2020 SPIE.

authors

• Cruz A.
• Flores J.L.
• González Contreras Francisco Javier
• Navarro N.E.
• Oceguera A.

publication date

• 2020-01-01

funding provided via

• Consejo Nacional de Ciencia y Tecnología, CONACYT: 461548  Grant
• Fondo Nacional de Ciencia y Tecnología, FONACYT  Grant

published in

• Proceedings of SPIE - The International Society for Optical Engineering  Proceedings

keywords

• Breast cancer screening; Deep convolutional neural networks; Infrared thermography; Transfer learning Convolution; Deep neural networks; Diseases; Image processing; Temperature measuring instruments; Thermography (temperature measurement); Transfer learning; Activation mapping; Affected area; Automatic analysis; Automatic classification; Blood perfusion; Breast Cancer; Temperature patterns; Thermal anomalies; Convolutional neural networks

Digital Object Identifier (DOI)

• 10.1117/12.2568997

start page

end page

volume

• 11510