Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/585433
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dc.date.accessioned2024-08-27T10:04:38Z-
dc.date.available2024-08-27T10:04:38Z-
dc.identifier.urihttp://hdl.handle.net/10603/585433-
dc.description.abstractMachine learning is playing an instrumental role in medial image analysis. Medical newlineimage processing has provided exponential opportunities to the researchers from newlineinterdisciplinary fields (e.g. engineering, statistics, mathematics, medicine, and newlinephysics). The computer aided diagnosis is not a threat to the radiologist and newlinepathologist, but it is a decision support system to enhance accuracy and speed of newlinediagnosis. Emergence of medical technology evolved a variety of medical imaging newlinedevices to scan the internal anatomy of human body for early detection of abnormal newlinemedical conditions. Various imaging modalities are X-RAY, CT, MRI, PET and newlineultrasound. MRI scan is a high resolution image of bones, soft tissues, nerves and newlineblood vessels. In our research we mainly worked on MRI datasets... newline
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dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleMedical Image Analysis for Early Detection of Abnormal Medical Conditions using Machine Learning and Deep Learning Models
dc.title.alternative
dc.creator.researcherJain,Manju
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Interdisciplinary Applications
dc.subject.keywordEngineering and Technology
dc.description.note
dc.contributor.guideRai , C. S.
dc.publisher.placeDelhi
dc.publisher.universityGuru Gobind Singh Indraprastha University
dc.publisher.institutionUniversity School of Information and Communication Technology
dc.date.registered2016
dc.date.completed2024
dc.date.awarded2024
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:University School of Information and Communication Technology

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