Automatic analysis of breast thermograms by convolutional neural networks

Jorge L. Flores; Francisco J. Gonzalez; Adán Cruz; Nancy E. Navarro; Antonio Oceguera

doi:10.1117/12.2568997

21 August 2020 Automatic analysis of breast thermograms by convolutional neural networks

Jorge L. Flores, Francisco J. Gonzalez, Adán Cruz, Nancy E. Navarro, Antonio Oceguera

Proceedings Volume 11510, Applications of Digital Image Processing XLIII; 115101R (2020) https://doi.org/10.1117/12.2568997
Event: SPIE Optical Engineering + Applications, 2020, Online Only

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.

Conference Presentation

Citation Download Citation

Jorge L. Flores, Francisco J. Gonzalez, Adán Cruz, Nancy E. Navarro, and Antonio Oceguera "Automatic analysis of breast thermograms by convolutional neural networks", Proc. SPIE 11510, Applications of Digital Image Processing XLIII, 115101R (21 August 2020); https://doi.org/10.1117/12.2568997

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