Close
Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/47JU645
Repositorysid.inpe.br/sibgrapi/2022/09.10.19.35
Last Update2022:09.10.19.35.42 (UTC) arbackes@yahoo.com.br
Metadata Repositorysid.inpe.br/sibgrapi/2022/09.10.19.35.42
Metadata Last Update2023:05.23.04.20.42 (UTC) administrator
DOI10.1109/SIBGRAPI55357.2022.9991771
Citation KeyBackes:2022:PaImCl
TitlePap-smear image classification by using a fusion of texture features
Short TitlePap-smear image classification by using a fusion of texture features
FormatOn-line
Year2022
Access Date2025, May 11
Number of Files1
Size644 KiB
2. Context
AuthorBackes, André Ricardo
AffiliationSchool of Computer Science, Federal University of Uberlândia
e-Mail Addressarbackes@yahoo.com.br
Conference NameConference on Graphics, Patterns and Images, 35 (SIBGRAPI)
Conference LocationNatal, RN
Date24-27 Oct. 2022
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2022-09-10 19:35:42 :: arbackes@yahoo.com.br -> administrator ::
2023-05-23 04:20:42 :: administrator -> :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordstexture analysis
PSO
pap-smear
image classification
AbstractIn this paper we address the problem of pap-smear image classification. These images have great medical importance to diagnose and prevent uterine cervix cancer and have been intensively studied in computer vision research. We evaluated 19 texture features on their ability to discriminate between two classes (normal and abnormal) of pap-smear images. We performed the classification of these feature using three different approaches: K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Linear Discriminant Data (LDA). We conducted this evaluation considering each texture method independently and their concatenation with others. Results show combining methods improves the accuracy, surpassing most of the compared methods, including some deep learning approaches.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2022 > Pap-smear image classification by using a fusion of texture features
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 10/09/2022 16:35 1.6 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/47JU645
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/47JU645
Languageen
Target Filebackes_16.pdf
User Grouparbackes@yahoo.com.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/495MHJ8
Citing Item Listsid.inpe.br/sibgrapi/2023/05.19.12.10 43
sid.inpe.br/sibgrapi/2022/06.10.21.49 5
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition editor electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session sponsor subject tertiarymark type url versiontype volume


Close