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.
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References
Anderson, B.O., et al.: Breast J. 12(Suppl 1:S3-15) (2005). PMID: 16430397
Pérez, M.G., Conci, A., Aguilar, A., Sánchez, A., Andaluz, V.H.: Detección temprana del cáncer de mama mediante la termografía en Ecuador (2014)
Araújo, M.C., Lima, R.C.F., de Souza, R.M.C.R.: Interval symbolic feature extraction for thermography breast cancer detection (2014)
Gogoi, U.R., Majumdar, G., Bhowmik, M.K., Ghosh, A.K., Bhattacharjee, D.: Breast abnormality detection through statistical feature analysis using infrared thermograms (2015)
Mejía, T.M., Pérez, M.G., Andaluz, V.H., Conci, A.: Automatic segmentation and analysis of thermograms using texture descriptors for breast cancer detection (2015)
Silva, L.F., et al.: A new database for breast research with infrared image. Banco de imágenes Visual Lab (2014). http://visual.ic.uff.br/dmi
Acharya, U.R., Ng, E.Y.K., Tan, J.-H., Sree, S.V.: Thermography based breast cancer detection using texture features and support vector machine (2012)
Ali, M.A.S., Hassanien, A.E., Gaber, T., Silva, L.: Detection of breast abnormalities of thermograms based on a new segmentation method (2015)
Sathish, D., Kamath, S., Prasad, K., Kadavigere, R., Martis, R.J.: Asymmetry analysis of breast thermograms using automated segmentation and texture features (2016)
Guerrero, S.R., Loaiza, H., Retrepo, A.D.: Automatic segmentation of thermal images to support breast cáncer diagnosis (2014)
Kandlikar, S.G., et al.: Infrared imaging technology for breast cancer detection – Current status, protocols and new directions (2017)
Fernández‐Martínez, J.L., Xu, S., Sirieix, C., Fernández‐Muniz, Z., Riss, J.: Uncertainty analysis and probabilistic segmentation of electrical resistivity images: the 2D inverse problem. Geophys. Prospect. 65, 112–130 (2017)
Smith, L.N.: Cyclical learning rates for training neuronal networks (2014)
Takahashi, R., Matsubara, T., Uehara, K.: Data Augmentation using Random Image Cropping and Patching for Deep CNNs (2018)
Montone, G., O’Regan, J.K., Terekhov, A.V.: Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014)
Fernández-Muñiz, Z., Khaniani, H., Fernández-Martínez, J.L.: Data kit inversion and uncertainty analysis. J. Appl. Geophys. 161, 228–238 (2019)
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Fernández-Ovies, F.J., Santiago Alférez-Baquero, E., de Andrés-Galiana, E.J., Cernea, A., Fernández-Muñiz, Z., Fernández-Martínez, J.L. (2019). Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_46
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DOI: https://doi.org/10.1007/978-3-030-17935-9_46
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