Classification of Alzheimer's Disease in MRI based on Dictionary Learning and Heavy Tailed Modelling

Diagnosis of brain diseases is considered one of the most challenging medical tasks to perform, even for medical experts who rely on high-resolution anatomical images to identify signs of abnormalities by visual inspection. However, new computational tools which assist to automate this diagnosis hav...

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Bibliographic Details
Published in2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2019; pp. 454 - 457
Main Authors Mayo, Perla, Holmes, Robin, Achim, Alin
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2019
Subjects
Alzheimer Disease
Brain
Dictionaries
Diseases
Feature extraction
Humans
Learning
Machine learning
Magnetic Resonance Imaging
Support Vector Machine
Support vector machines
Task analysis
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Summary:Diagnosis of brain diseases is considered one of the most challenging medical tasks to perform, even for medical experts who rely on high-resolution anatomical images to identify signs of abnormalities by visual inspection. However, new computational tools which assist to automate this diagnosis have the potential to significantly improve the speed and accuracy of this process. This work presents a model to aid in the task of classification of structural Magnetic Resonance Imaging scans. The classification is performed using a Support Vector Machine, whilst the features to analyze belong to a dictionary space. Such space was mainly built from a dictionary learning perspective, although a predefined one was also assessed. The results indicate that features learnt from the data of interest lead to improved classification performance. The proposed framework was tested on the ADNI dataset stage I.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2019.8857379