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3D - Patch Based Machine Learning Sy...

Srivastava, Anant.

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3D - Patch Based Machine Learning Systems for Alzheimer's Disease classi cation via 18F-FDG PET Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	3D - Patch Based Machine Learning Systems for Alzheimer's Disease classi cation via 18F-FDG PET Analysis./
Author:	Srivastava, Anant.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	78 p.
Notes:	Source: Masters Abstracts International, Volume: 56-04.
Contained By:	Masters Abstracts International56-04(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10275736
ISBN:	9781369756739

3D - Patch Based Machine Learning Systems for Alzheimer's Disease classi cation via 18F-FDG PET Analysis.
Srivastava, Anant.

3D - Patch Based Machine Learning Systems for Alzheimer's Disease classi cation via 18F-FDG PET Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 78 p.

Source: Masters Abstracts International, Volume: 56-04.

Thesis (M.S.)--Arizona State University, 2017.

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 preferred imaging modality, this thesis develops two independent machine learning based patch analysis methods and uses them to perform six binary classification experiments across different (AD) diagnostic categories. Specifically, features were extracted and learned using dimensionality reduction and dictionary learning & sparse coding by taking overlapping patches in and around the cerebral cortex and using them as features. Using AdaBoost as the preferred choice of classifier both methods try to utilize 18F-FDG PET as a biological marker in the early diagnosis of Alzheimer's. Additional we investigate the involvement of rich demographic features (Apoe E3, ApoeE4 and Functional Activities Questionnaires (FAQ)) in classification. The experimental results on Alzheimer's Disease Neuroimaging initiative (ADNI) dataset demonstrate the effectiveness of both the proposed systems. The use of 18F-FDG PET may offer a new sensitive biomarker and enrich the brain imaging analysis toolset for studying the diagnosis and prognosis of AD.

ISBN: 9781369756739Subjects--Topical Terms:

523869
Computer science.

3D - Patch Based Machine Learning Systems for Alzheimer's Disease classi cation via 18F-FDG PET Analysis.
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