TY - GEN
T1 - Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks
AU - Fernández-Ovies, Francisco Javier
AU - Santiago Alférez-Baquero, Edwin
AU - de Andrés-Galiana, Enrique Juan
AU - Cernea, Ana
AU - Fernandez Martinez, Zulima
AU - Fernandez Martinez, Juan Luis
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - We present a preliminary analysis about the use of convolutional neural networks (CNNs) for the early detection of breast cancer via infrared thermography. The two main challenges of using CNNs are having at disposal a large set of images and the required processing time. The thermographies were obtained from Vision Lab and the calculations were implemented using Fast.ai and Pytorch libraries, which offer excellent results in image classification. Different architectures of convolutional neural networks were compared and the best results were obtained with resnet34 and resnet50, reaching a predictive accuracy of 100% in blind validation. Other arquitectures also provided high classification accuracies. Deep neural networks provide excellent results in the early detection of breast cancer via infrared thermographies, with technical and computational resources that can be easily implemented in medical practice. Further research is needed to asses the probabilistic localization of the tumor regions using larger sets of annotated images and assessing the uncertainty of these techniques in the diagnosis.
AB - We present a preliminary analysis about the use of convolutional neural networks (CNNs) for the early detection of breast cancer via infrared thermography. The two main challenges of using CNNs are having at disposal a large set of images and the required processing time. The thermographies were obtained from Vision Lab and the calculations were implemented using Fast.ai and Pytorch libraries, which offer excellent results in image classification. Different architectures of convolutional neural networks were compared and the best results were obtained with resnet34 and resnet50, reaching a predictive accuracy of 100% in blind validation. Other arquitectures also provided high classification accuracies. Deep neural networks provide excellent results in the early detection of breast cancer via infrared thermographies, with technical and computational resources that can be easily implemented in medical practice. Further research is needed to asses the probabilistic localization of the tumor regions using larger sets of annotated images and assessing the uncertainty of these techniques in the diagnosis.
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U2 - 10.1007/978-3-030-17935-9_46
DO - 10.1007/978-3-030-17935-9_46
M3 - Conference contribution
AN - SCOPUS:85065722081
SN - 9783030179342
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 514
EP - 523
BT - Bioinformatics and Biomedical Engineering - 7th International Work-Conference, IWBBIO 2019, Proceedings
A2 - Rojas, Fernando
A2 - Rojas, Ignacio
A2 - Ortuño, Francisco
A2 - Ortuño, Francisco
A2 - Valenzuela, Olga
PB - Springer
T2 - 7th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2019
Y2 - 8 May 2019 through 10 May 2019
ER -
