@InProceedings{BaffaLatt:2018:CoNeNe,
author = "Baffa, Matheus de Freitas Oliveira and Lattari, Lucas Grassano",
affiliation = "Instituto Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e
Tecnologia do Sudeste de Minas Gerais and Instituto Federal de
Educa{\c{c}}{\~a}o, Ci{\^e}ncia e Tecnologia do Sudeste de
Minas Gerais",
title = "Convolutional Neural Networks for Static and Dynamic Breast
Infrared Imaging Classification",
booktitle = "Proceedings...",
year = "2018",
editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and
Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and
Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez,
Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de
and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa,
Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus,
Klaus de and Scheer, Sergio",
organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "breast cancer, computer-aided diagnosis, convolutional neural
network, deep learning.",
abstract = "Breast cancer is the most frequent type of cancer among women.
Since early diagnosis provides a better prognosis, different
techniques have been developed by researchers all over the world.
Several studies proved the efficiency of infrared image as a
breast cancer screening technique. This paper proposes a
methodology for analyzing infrared thermography of breast,
considering distinct protocols, in order to classify patients
images as healthy or non-healthy due to anomalies such as cancer.
The major contribution of this work is to provide accurate
classification using Convolutional Neural Networks, which were not
exploited in previous works. Many methods relies on handcrafted
features and traditional classificators, such as Support Vector
Machines. We obtained competitive results compared to other works
and we design an appropriate modelling which takes advantage of
this type of deep learning architecture. Our proposal obtained 98%
of accuracy for static protocol and 95% for dynamic protocol.",
conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
conference-year = "29 Oct.-1 Nov. 2018",
doi = "10.1109/SIBGRAPI.2018.00029",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00029",
language = "en",
ibi = "8JMKD3MGPAW/3RPBCBB",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3RPBCBB",
targetfile = "96.pdf",
urlaccessdate = "2024, Apr. 29"
}