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Convolutional Neural Network Based Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Technique using Hippocampus Extracted from MRIMUKHTAR, G.   , FARHAN, S. |
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Author keywords
artificial neural networks, computer aided diagnosis, image analysis, image classification, pattern recognition
References keywords
alzheimer(35), disease(29), cognitive(13), prediction(12), mild(12), impairment(12), conversion(11), classification(10), brain(10), neuroimage(9)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2020-05-31
Volume 20, Issue 2, Year 2020, On page(s): 113 - 122
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.02013
Web of Science Accession Number: 000537943500013
SCOPUS ID: 85087437924
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder. Mild Cognitive Impairment (MCI) is a prodromal stage of AD and its identification is very crucial for early treatment. MCI to AD conversion is of imperative concern in current Alzheimer's research. In this study, we have investigated the conversion from MCI to AD using different types of features. The impact of structural changes in entire brain tissues captured through MRI, genetics, neuropsychological assessment scores and their combination are investigated. Computational cost can be significantly reduced by examining only the hippocampi region, atrophy of which is visible in the earliest stages of the disease. We proposed a CNN based deep learning approach for the prediction of conversion from MCI to AD using above mentioned features. Highest accuracy is achieved when left hippocampus is used as a region of interest (ROI). The proposed technique outperforms the other state of the art methods, while maintaining a low computational cost. The main contribution of the research lies in the fact that only a single slice based small region of MRI is used resulting in an outstanding performance. The accuracy, sensitivity and specificity achieved are 94%, 92% and 96% respectively. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
