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3D - Patch Based Machine Learning Systems for Alzheimer’s Disease classification via 18F-FDG PET Analysis

Abstract Alzheimer’s disease (AD), is a chronic neurodegenerative disease that usually starts slowly and gets worse over time. It is the cause of 60% to 70% of cases of dementia. There is growing interest in identifying brain image biomarkers that help evaluate AD risk pre-symptomatically. High-dimensional non-linear pattern classification methods have been applied to structural magnetic resonance images (MRI’s) and used to discriminate between clinical groups in Alzheimers progression. Using Fluorodeoxyglucose (FDG) positron emission tomography (PET) as the pre- ferred imaging modality, this thesis develops two independent machine learning based patch analysis methods and uses them to perform six binary classification experiments across different (... (more)
Created Date 2017
Contributor Srivastava, Anant (Author) / Wang, Yalin (Advisor) / Bansal, Ajay (Advisor) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Subject Computer science / Medical imaging / Neurosciences / adaboost classifier / alzheimer's disease / dimensionality reduction / machine learning / max-pooling / sparse coding
Type Masters Thesis
Extent 78 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis
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Description Thesis Defense Presentation