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		<citationkey>BaffaLatt:2018:CoNeNe</citationkey>
		<title>Convolutional Neural Networks for Static and Dynamic Breast Infrared Imaging Classification</title>
		<format>On-line</format>
		<year>2018</year>
		<date>Oct. 29 - Nov. 1, 2018</date>
		<numberoffiles>1</numberoffiles>
		<size>6091 KiB</size>
		<author>Baffa, Matheus de Freitas Oliveira,</author>
		<author>Lattari, Lucas Grassano,</author>
		<affiliation>Instituto Federal de Educação, Ciência e Tecnologia do Sudeste de Minas Gerais</affiliation>
		<affiliation>Instituto Federal de Educação, Ciência e Tecnologia do Sudeste de Minas Gerais</affiliation>
		<editor>Ross, Arun,</editor>
		<editor>Gastal, Eduardo S. L.,</editor>
		<editor>Jorge, Joaquim A.,</editor>
		<editor>Queiroz, Ricardo L. de,</editor>
		<editor>Minetto, Rodrigo,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Oliveira, Manuel M.,</editor>
		<editor>Arbeláez, Pablo,</editor>
		<editor>Mery, Domingo,</editor>
		<editor>Oliveira, Maria Cristina Ferreira de,</editor>
		<editor>Spina, Thiago Vallin,</editor>
		<editor>Mendes, Caroline Mazetto,</editor>
		<editor>Costa, Henrique Sérgio Gutierrez,</editor>
		<editor>Mejail, Marta Estela,</editor>
		<editor>Geus, Klaus de,</editor>
		<editor>Scheer, Sergio,</editor>
		<e-mailaddress>mfreitas826@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)</conferencename>
		<conferencelocation>Foz do Iguaçu, PR, Brazil</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<documentstage>not transferred</documentstage>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<tertiarytype>Full Paper</tertiarytype>
		<keywords>breast cancer, computer-aided diagnosis, convolutional neural network, deep learning.</keywords>
		<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.</abstract>
		<language>en</language>
		<targetfile>96.pdf</targetfile>
		<usergroup>mfreitas826@gmail.com</usergroup>
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