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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3MC992S
Repositorysid.inpe.br/sibgrapi/2016/09.01.15.04
Last Update2016:09.01.15.04.23 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/09.01.15.04.23
Metadata Last Update2022:05.18.22.21.09 (UTC) administrator
Citation KeyCíceroOlivBote:2016:DeLeCo
TitleDeep Learning and Convolutional Neural Networks in the Aid of the Classification of Melanoma
FormatOn-line
Year2016
Access Date2024, Apr. 29
Number of Files1
Size598 KiB
2. Context
Author1 Cícero, Felipe Moure
2 Oliveira, Ary Henrique
3 Botelho, Glenda
Affiliation1 Universidade Federal do Tocantins
2 Universidade Federal do Tocantins
3 Universidade Federal do Tocantins
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
e-Mail Addressfelipecicero@outlook.com
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeUndergraduate Work
History (UTC)2016-09-01 15:04:23 :: felipecicero@outlook.com -> administrator ::
2022-05-18 22:21:09 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsdeep learning
convolutional neural networks
melanoma classification
AbstractPattern 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).
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2016 > Deep Learning and...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3MC992S
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3MC992S
Languageen
Target File16.pdf
User Groupfelipecicero@outlook.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 3
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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