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Reference TypeConference Proceedings
Identifier8JMKD3MGPAW/3RPBCBB
Repositorysid.inpe.br/sibgrapi/2018/09.04.01.45
Metadatasid.inpe.br/sibgrapi/2018/09.04.01.45.34
Sitesibgrapi.sid.inpe.br
Citation KeyBaffaLatt:2018:CoNeNe
Author1 Baffa, Matheus de Freitas Oliveira
2 Lattari, Lucas Grassano
Affiliation1 Instituto Federal de Educação, Ciência e Tecnologia do Sudeste de Minas Gerais
2 Instituto Federal de Educação, Ciência e Tecnologia do Sudeste de Minas Gerais
TitleConvolutional Neural Networks for Static and Dynamic Breast Infrared Imaging Classification
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Year2018
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Book TitleProceedings
DateOct. 29 - Nov. 1, 2018
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationFoz do Iguaçu, PR, Brazil
Keywordsbreast cancer, computer-aided diagnosis, convolutional neural network, deep learning.
AbstractBreast 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.
Languageen
Tertiary TypeFull Paper
FormatOn-line
Size6091 KiB
Number of Files1
Target File96.pdf
Last Update2018:09.04.01.45.34 sid.inpe.br/banon/2001/03.30.15.38 administrator
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History2018-09-04 01:45:34 :: mfreitas826@gmail.com -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
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