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  2/2020 - 13

 HIGH-IMPACT PAPER 

Convolutional Neural Network Based Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Technique using Hippocampus Extracted from MRI

MUKHTAR, G. See more information about MUKHTAR, G. on SCOPUS See more information about MUKHTAR, G. on IEEExplore See more information about MUKHTAR, G. on Web of Science, FARHAN, S. See more information about FARHAN, S. on SCOPUS See more information about FARHAN, S. on SCOPUS See more information about FARHAN, S. on Web of Science
 
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Download PDF pdficon (1,466 KB) | Citation | Downloads: 1,082 | Views: 2,399

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
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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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References Weight

Web of Science® Citations for all references: 49,145 TCR
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Web of Science® Average Citations per reference: 910 ACR
SCOPUS® Average Citations per reference: 1,082 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

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