Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/546491
Full metadata record
DC FieldValueLanguage
dc.coverage.spatialEfficient classification and segmentation of brain stroke mri images using enhanced convolutional neural networks
dc.date.accessioned2024-02-21T11:16:23Z-
dc.date.available2024-02-21T11:16:23Z-
dc.identifier.urihttp://hdl.handle.net/10603/546491-
dc.description.abstractnewline In today s world, stroke is a life-threatening disease requiring newlineimmediate medical attention to prevent permanent brain damage or death. newlineWorldwide, strokes rank second among the top causes of death. When there is newlinea difficulty with blood flow to a certain area of the brain, it happens when a newlineblood vessel is blocked or when the brain is bleeding. Anybody can have a newlinestroke, from children to adults and, more common in people over 65. According newlineto WHO research, 15 million people worldwide suffer from strokes yearly, of newlinewhich 5 million deaths occur and more than 5 million become permanently newlinedisabled. In persons under 40, high blood pressure is the main contributor to newlinestroke. However, around 8% of children also experience strokes. The greatest newlinecause of disability, particularly in low- and middle-income nations, is stroke. newlineThis research focuses on enhancing stroke diagnosis through newlineadvanced medical image processing techniques, addressing the limitations of newlineexisting methods. The diagnosis of brain diseases has been made by medical newlineimaging methods such as CT, MRI, and PET. Compared to CT, Magnetic newlineresonance imaging (MRI) is a valuable tool to identify many vascular and brain newlineabnormalities, and it is capable of producing multiplanar images that help to newlinevisualize the brain in different orientations, providing a more comprehensive newlineunderstanding of the location and extent of brain damage caused by ischemic newlineand hemorrhagic stroke.
dc.format.extentxxii,184p.
dc.languageEnglish
dc.relationp.172-183
dc.rightsuniversity
dc.titleEfficient classification and segmentation of brain stroke mri images using enhanced convolutional neural networks
dc.title.alternative
dc.creator.researcherShakunthala, M
dc.subject.keywordEngineering
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering Electrical and Electronic
dc.description.note
dc.contributor.guideHelenprabha, K
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Electrical Engineering
dc.date.registered
dc.date.completed2024
dc.date.awarded2024
dc.format.dimensions21cm.
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Electrical Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File4.98 MBAdobe PDFView/Open
02_prelimpages.pdf2.3 MBAdobe PDFView/Open
03_content.pdf4.98 MBAdobe PDFView/Open
04_abstract.pdf4.98 MBAdobe PDFView/Open
05_chapter1.pdf4.98 MBAdobe PDFView/Open
06_chapter2.pdf5.07 MBAdobe PDFView/Open
07_chapter3.pdf5.06 MBAdobe PDFView/Open
08_chapter4.pdf5.13 MBAdobe PDFView/Open
09_chapter5.pdf5.2 MBAdobe PDFView/Open
10_chapter6.pdf5.4 MBAdobe PDFView/Open
11_annexures.pdf510.49 kBAdobe PDFView/Open
80_recommendation.pdf64.05 kBAdobe PDFView/Open


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: