Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks

Francisco Javier Fern&#x00E1;ndez-Ovies; Edwin Santiago Alf&#x00E9;rez-Baquero; Enrique Juan de Andr&#x00E9;s-Galiana; Ana Cernea; Zulima Fernandez Martinez; Juan Luis Fernandez Martinez

doi:10.1007/978-3-030-17935-9_46

Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks

Francisco Javier Fernández-Ovies
, Edwin Santiago Alférez-Baquero
, Enrique Juan de Andrés-Galiana
, Ana Cernea
, Zulima Fernandez Martinez
, Juan Luis Fernandez Martinez

Research output: Chapter in Book/Inform › Conference contribution

40 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Bioinformatics and Biomedical Engineering - 7th International Work-Conference, IWBBIO 2019, Proceedings
Editors	Fernando Rojas, Ignacio Rojas, Francisco Ortuño, Francisco Ortuño, Olga Valenzuela
Publisher	Springer
Pages	514-523
Number of pages	10
ISBN (Print)	9783030179342
DOIs	https://doi.org/10.1007/978-3-030-17935-9_46
State	Published - 2019
Externally published	Yes
Event	7th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2019 - Granada, Spain Duration: May 8 2019 → May 10 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11466 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2019
Country/Territory	Spain
City	Granada
Period	5/8/19 → 5/10/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-17935-9_46

Cite this

Fernández-Ovies, F. J., Santiago Alférez-Baquero, E., de Andrés-Galiana, E. J., Cernea, A., Fernandez Martinez, Z., & Fernandez Martinez, J. L. (2019). Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks. In F. Rojas, I. Rojas, F. Ortuño, F. Ortuño, & O. Valenzuela (Eds.), Bioinformatics and Biomedical Engineering - 7th International Work-Conference, IWBBIO 2019, Proceedings (pp. 514-523). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11466 LNBI). Springer. https://doi.org/10.1007/978-3-030-17935-9_46

Fernández-Ovies, Francisco Javier ; Santiago Alférez-Baquero, Edwin ; de Andrés-Galiana, Enrique Juan et al. / Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks. Bioinformatics and Biomedical Engineering - 7th International Work-Conference, IWBBIO 2019, Proceedings. editor / Fernando Rojas ; Ignacio Rojas ; Francisco Ortuño ; Francisco Ortuño ; Olga Valenzuela. Springer, 2019. pp. 514-523 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{8abb1d8638364b0b97ae67bb40152492,

title = "Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks",

abstract = "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.",

author = "Fern\&\#x00E1;ndez-Ovies, \{Francisco Javier\} and \{Santiago Alf\&\#x00E9;rez-Baquero\}, Edwin and \{de Andr\&\#x00E9;s-Galiana\}, \{Enrique Juan\} and Ana Cernea and \{Fernandez Martinez\}, Zulima and \{Fernandez Martinez\}, \{Juan Luis\}",

note = "Publisher Copyright: \&\#x00A9; 2019, Springer Nature Switzerland AG. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 7th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2019 ; Conference date: 08-05-2019 Through 10-05-2019",

year = "2019",

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language = "English (US)",

isbn = "9783030179342",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "514--523",

editor = "Fernando Rojas and Ignacio Rojas and Francisco Ortu{\~n}o and Francisco Ortu{\~n}o and Olga Valenzuela",

booktitle = "Bioinformatics and Biomedical Engineering - 7th International Work-Conference, IWBBIO 2019, Proceedings",

address = "Germany",

}

Fernández-Ovies, FJ, Santiago Alférez-Baquero, E, de Andrés-Galiana, EJ, Cernea, A, Fernandez Martinez, Z & Fernandez Martinez, JL 2019, Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks. in F Rojas, I Rojas, F Ortuño, F Ortuño & O Valenzuela (eds), Bioinformatics and Biomedical Engineering - 7th International Work-Conference, IWBBIO 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11466 LNBI, Springer, pp. 514-523, 7th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2019, Granada, Spain, 5/8/19. https://doi.org/10.1007/978-3-030-17935-9_46

Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks. / Fernández-Ovies, Francisco Javier; Santiago Alférez-Baquero, Edwin; de Andrés-Galiana, Enrique Juan et al.
Bioinformatics and Biomedical Engineering - 7th International Work-Conference, IWBBIO 2019, Proceedings. ed. / Fernando Rojas; Ignacio Rojas; Francisco Ortuño; Francisco Ortuño; Olga Valenzuela. Springer, 2019. p. 514-523 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11466 LNBI).

Research output: Chapter in Book/Inform › Conference contribution

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

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.

UR - https://www.scopus.com/pages/publications/85065722081

UR - https://www.scopus.com/pages/publications/85065722081#tab=citedBy

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 - 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 -

Fernández-Ovies FJ, Santiago Alférez-Baquero E, de Andrés-Galiana EJ, Cernea A, Fernandez Martinez Z, Fernandez Martinez JL. Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks. In Rojas F, Rojas I, Ortuño F, Ortuño F, Valenzuela O, editors, Bioinformatics and Biomedical Engineering - 7th International Work-Conference, IWBBIO 2019, Proceedings. Springer. 2019. p. 514-523. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-17935-9_46

Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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