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		<citationkey>CíceroOlivBote:2016:DeLeCo</citationkey>
		<title>Deep Learning and Convolutional Neural Networks in the Aid of the Classification of Melanoma</title>
		<format>On-line</format>
		<year>2016</year>
		<numberoffiles>1</numberoffiles>
		<size>598 KiB</size>
		<author>Cícero, Felipe Moure,</author>
		<author>Oliveira, Ary Henrique,</author>
		<author>Botelho, Glenda,</author>
		<affiliation>Universidade Federal do Tocantins</affiliation>
		<affiliation>Universidade Federal do Tocantins</affiliation>
		<affiliation>Universidade Federal do Tocantins</affiliation>
		<editor>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, João Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>felipecicero@outlook.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>São José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Undergraduate Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>deep learning, convolutional neural networks, melanoma classification.</keywords>
		<abstract>Pattern recognition in digital images is a major limitation in machine learning area. But, in recent years, deep learning has rapidly been diffused, providing large advancements in visual computing by solving the main problems that machine learning imposes. Based on these advances, this study aims to improve results of a problem well-known by visual computing, the classification of melanoma, this one is classified as a malignant tumor, highly invasive and easily confused with other skin diseases. To achieve this, we use some techniques of deep learning to try to get better results in the task of classifying whether a melanotic lesion is the malignant type (melanoma) or not (nevus). In this work we present a training approach using a custom dataset of skin diseases, transfer learning, convolutional neural networks and data augmentation of the deep network ResNet (Deep Residual Network).</abstract>
		<language>en</language>
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