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dc.contributor.authorPunugu, Venkatapavani Pallavi
dc.date.accessioned2018-05-23T20:18:24Z
dc.date.available2018-05-23T20:18:24Z
dc.date.issued2017
dc.identifier.isbn9780355047462
dc.identifier.other1926752803
dc.identifier.urihttp://hdl.handle.net/10477/77301
dc.description.abstractThe 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%.
dc.languageEnglish
dc.sourceDissertations & Theses @ SUNY Buffalo,ProQuest Dissertations & Theses Global
dc.subjectHealth and environmental sciences
dc.subjectArtificial neural networks
dc.subjectK-nearest neighbors
dc.subjectMachine learning
dc.subjectNeuroimaging
dc.subjectSPECT
dc.subjectSupport vector machines
dc.titleMachine Learning in Neuroimaging
dc.typeDissertation/Thesis


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