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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3M4UBD2
Repositorysid.inpe.br/sibgrapi/2016/07.18.18.27
Last Update2016:07.18.18.27.00 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/07.18.18.27.01
Metadata Last Update2022:06.14.00.08.25 (UTC) administrator
DOI10.1109/SIBGRAPI.2016.061
Citation KeyFelixFerrOlivMach:2016:Us3DTe
TitleUsing 3D Texture and Margin Sharpness Features on Classification of Small Pulmonary Nodules
FormatOn-line
Year2016
Access Date2024, May 03
Number of Files1
Size409 KiB
2. Context
Author1 Felix, Ailton de Lima Filho
2 Ferreira Junior, José Raniery
3 Oliveira, Marcelo Costa
4 Machado, Aydano Pamponet
Affiliation1 Universidade Federal de Alagoas
2 Universidade de São Paulo
3 Universidade Federal de Alagoas
4 Universidade Federal de Alagoas
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 Addressaflf@ic.ufal.br
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherIEEE Computer Society´s Conference Publishing Services
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2016-07-18 18:27:01 :: aflf@ic.ufal.br -> administrator ::
2016-10-05 14:49:12 :: administrator -> aflf@ic.ufal.br :: 2016
2016-10-14 19:16:17 :: aflf@ic.ufal.br -> administrator :: 2016
2022-06-14 00:08:25 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordslung cancer
small nodules
early diagnosis
computer-aided diagnosis
texture features
margin sharpness features
classification
machine learning
AbstractThe lung cancer is the reason of a lot of deaths on population around the world. An early diagnosis brings a most curable and simpler treatment options. Due to complexity diagnosis of small pulmonary nodules, Computer-Aided Diagnosis (CAD) tools provides an assistance to radiologist aiming the improvement in the diagnosis. Extracting relevant image features is of great importance for these tools. In this work we extracted 3D Texture Features (TF) and 3D Margin Sharpness Features (MSF) from the Lung Image Database Consortium (LIDC) in order to create a classification model to classify small pulmonary nodules with diameters between 3-10mm. We used three machine learning algorithm: k-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP) and Random Forest (RF). These algorithms were trained by different set of features from the TF and MSF. The classification model with MLP algorithm using the selected features from the integration of TF and MSF achieved the best AUC of 0.820.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2016 > Using 3D Texture...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Using 3D Texture...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 18/07/2016 15:27 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3M4UBD2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3M4UBD2
Languageen
Target FilePID4357869.pdf
User Groupaflf@ic.ufal.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 5
sid.inpe.br/sibgrapi/2022/06.10.21.49 2
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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