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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/43AH6HP
Repositorysid.inpe.br/sibgrapi/2020/09.24.19.20
Last Update2020:09.24.19.20.59 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.24.19.20.59
Metadata Last Update2022:05.18.22.18.39 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00029
Citation KeyPaz-SotoHeroFernDíaz:2020:AuClEr
TitleAutomatic Classification of Erythrocytes Using Artificial Neural Networks and Integral Geometry-Based Functions
FormatOn-line
Year2020
Access Date2022, May 21
Number of Files1
Size609 KiB
2. Context
Author1 Paz-Soto, Yaima
2 Herold-Garcia, Silena
3 Fernandes, Leandro A. F.
4 Díaz-Matos, Saul
Affiliation1 Universidad de Guántanamo
2 Universidad de Oriente
3 Universidade Federal Fluminense
4 Universidad de Oriente
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addresslaffernandes@ic.uff.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationVirtual
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-09-24 19:20:59 :: laffernandes@ic.uff.br -> administrator ::
2022-05-18 22:18:39 :: administrator -> laffernandes@ic.uff.br :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordssickle cell disease
integral geometry
artificial neural networks
shape descriptor
classification
AbstractThe red blood cell deformation caused by disorders like sickle cell disease can be assessed by observing blood samples under a microscope. This manual process is cumbersome and prone to errors but can be supported by automated techniques that allow red blood cells to be classified according to the shape they present. There are proposals in the literature that use functions based on integral geometry to obtain a description of the cells' contour before performing classification, reaching 96.16% accuracy with the use of the k-Nearest Neighbor (KNN) classifier. In those approaches, the classification-confusion cases persist mainly in the classes of most significant interest, which are those related to the detection of deformed cells. In this work, we use artificial neural networks-based classifiers, trained with the characteristics obtained from integral geometry-based functions, to classify erythrocytes into normal, sickle, and other deformations classes. Our proposal achieves accuracy of 98.40%. This result is superior to those of previous studies concerning the classes of greatest interest. Also, our approach is computationally more efficient than previous works, making it suitable for supporting medical follow-up diagnosis of sickle cell disease.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/43AH6HP
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/43AH6HP
Languageen
Target FilePaper 43.pdf
User Grouplaffernandes@ic.uff.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsaccessionnumber archivingpolicy 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 secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume
7. Description control
e-Mail (login)laffernandes@ic.uff.br
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