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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFR4G8
Repositorysid.inpe.br/sibgrapi/2017/08.21.20.07
Last Update2017:08.21.20.07.39 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.21.20.07.39
Metadata Last Update2022:06.14.00.08.56 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.29
Citation KeyRodriguesNaldMari:2017:ExCoNe
TitleExploiting Convolutional Neural Networks and preprocessing techniques for HEp-2 cell classification in immunofluorescence images
FormatOn-line
Year2017
Access Date2024, May 01
Number of Files1
Size1845 KiB
2. Context
Author1 Rodrigues, Larissa Ferreira
2 Naldi, Murilo Coelho
3 Mari, João Fernando
Affiliation1 Universidade Federal de Viçosa
2 Universidade Federal de Viçosa
3 Universidade Federal de Viçosa
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressjoaofmari@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-21 20:07:39 :: joaofmari@gmail.com -> administrator ::
2022-06-14 00:08:56 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsConvolutional neural networks
HEp-2 cells
staining patterns classification
LeNet-5
AlexNet
GoogLeNet
pre-processing
data augmentation
AbstractAutoimmune diseases are the third cause of mortality in the world. The identification of anti-nuclear antibody (ANA) via Immunofluorescence (IIF) test in human epithelial type-2 cells (HEp-2) is a conventional method to support the diagnosis of such diseases. In the present work, three popular Convolutional Neural Networks (CNNs) are evaluated for this task: LeNet-5, AlexNet, and GoogLeNet. We also assess the impact of six different pre-processing strategies on the performance of these CNNs. Additionally, data augmentation based on the rotation of the training set images after the pre-processing strategies was evaluated. Our work is the first to consider AlexNet and GoogLeNet models for the proposed analysis and classification of HEp-2 cells images, besides the LeNet-5. Experimental results allow to conclude that neither pre-processing strategies were essential to improve accuracy values of the CNNs. However, when data augmentation is considered, contrast enhancement followed by data centralization is significant in order to achieve good results. Additionally, our results were compared with results from other state-of-art papers. Our best results were achieved by GoogLeNet architecture trained with images with no pre-processing and no data augmentation, resulting in 98.17% of accuracy, which outperforms the results presented in other works in literature.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFR4G8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFR4G8
Languageen
Target FilePID4960235.pdf
User Groupjoaofmari@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 7
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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 volume


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