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Dissertation/Thesis Abstract

Machine Learning in Neuroimaging
by Punugu, Venkatapavani Pallavi, M.S., State University of New York at Buffalo, 2017, 41; 10284048
Abstract (Summary)

The application of machine learning algorithms to analyze and determine disease related patterns in neuroimaging has emerged to be of extreme interest in Computer-Aided Diagnosis (CAD). This study is a small step towards categorizing Alzheimer's disease, Neurode-generative diseases, Psychiatric diseases and Cerebrovascular Small Vessel diseases using CAD. In this study, the SPECT neuroimages are pre-processed using powerful data reduction techniques such as Singular Value Decomposition (SVD), Independent Component Analysis (ICA) and Automated Anatomical Labeling (AAL). Each of the pre-processing methods is used in three machine learning algorithms namely: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and k-Nearest Neighbors (k-nn) to recognize disease patterns and classify the diseases. While neurodegenerative diseases and psychiatric diseases overlap with a mix of diseases and resulted in fairly moderate classification, the classification between Alzheimer's disease and Cerebrovascular Small Vessel diseases yielded good results with an accuracy of up to 73.7%.

Indexing (document details)
Advisor: Wack, David S.
Commitee: Ionita, Ciprian N., Miletich, Robert S., Slavakis, Konstantinos
School: State University of New York at Buffalo
Department: Biomedical Engineering
School Location: United States -- New York
Source: MAI 56/05M(E), Masters Abstracts International
Subjects: Medical imaging
Keywords: Artificial neural networks, K-nearest neighbors, Machine learning, Neuroimaging, SPECT, Support vector machines
Publication Number: 10284048
ISBN: 978-0-355-04746-2
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