Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/180800
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DC FieldValueLanguage
dc.coverage.spatialElectrical and Electronics Engineering
dc.date.accessioned2017-11-13T10:47:47Z-
dc.date.available2017-11-13T10:47:47Z-
dc.identifier.urihttp://hdl.handle.net/10603/180800-
dc.description.abstractAvailable Abstract
dc.format.extentn.d.
dc.languageEnglish
dc.relation-
dc.rightsuniversity
dc.titleAbnor mality detection in ultrasound kidney images using machine learning techniques
dc.title.alternative-
dc.creator.researcherBaby, Christina
dc.subject.keywordAbnor mality
dc.subject.keywordmachine
dc.subject.keywordtechniques
dc.description.noteReference given
dc.contributor.guideParthiban, Latha
dc.publisher.placeChennai
dc.publisher.universitySt. Peters University
dc.publisher.institutionDepartment Of Electrical and Electronics Engineering
dc.date.registeredn.d
dc.date.completed2016
dc.date.awardedn.d
dc.format.dimensions-
dc.format.accompanyingmaterialNone
dc.type.degreePh.D.
dc.source.inflibnetINFLIBNET
Appears in Departments:Department Of Electrical and Electronics Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File21.75 kBAdobe PDFView/Open
02_certificate.pdf7.1 kBAdobe PDFView/Open
03_declaration.pdf6.96 kBAdobe PDFView/Open
04_acknowledgement.pdf8.51 kBAdobe PDFView/Open
05_abstract.pdf10.04 kBAdobe PDFView/Open
06_contents.pdf37.52 kBAdobe PDFView/Open
07_chapter 1.pdf286.02 kBAdobe PDFView/Open
08_chapter 2.pdf527.17 kBAdobe PDFView/Open
09_chapter 3.pdf422.79 kBAdobe PDFView/Open
10_chapter 4.pdf124.49 kBAdobe PDFView/Open
11_chapter 5.pdf313.03 kBAdobe PDFView/Open
12_chapter 6.pdf287.43 kBAdobe PDFView/Open
13_chapter 7.pdf18.28 kBAdobe PDFView/Open
14_reference.pdf78.7 kBAdobe PDFView/Open


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