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Print ISSN: 1582-7445
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doi: 10.4316/AECE


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  2/2017 - 15

A Novel Approach for the Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's disease using MRI Images

AYUB, A. See more information about AYUB, A. on SCOPUS See more information about AYUB, A. on IEEExplore See more information about AYUB, A. 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, FAHIEM, M. A. See more information about  FAHIEM, M. A. on SCOPUS See more information about  FAHIEM, M. A. on SCOPUS See more information about FAHIEM, M. A. on Web of Science, TAUSEEF, H. See more information about TAUSEEF, H. on SCOPUS See more information about TAUSEEF, H. on SCOPUS See more information about TAUSEEF, H. on Web of Science
 
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Download PDF pdficon (1,667 KB) | Citation | Downloads: 672 | Views: 1,000

Author keywords
computer aided diagnosis, feature extraction, image analysis, image classification, pattern recognition

References keywords
alzheimer(34), disease(29), classification(20), brain(14), neuroimage(13), cognitive(13), structural(12), mild(12), impairment(12), pattern(11)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-05-31
Volume 17, Issue 2, Year 2017, On page(s): 113 - 122
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.02015
Web of Science Accession Number: 000405378100015
SCOPUS ID: 85020067022

Abstract
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The main objective of our research is to introduce an approach that uses noninvasive MRI images to predict the conversion from mild cognitive impairment to Alzheimer's disease at an early stage. It detects normal controls that are likely to develop Alzheimer's disease and mild cognitive impairment patients that are likely to establish Alzheimer's disease within two years or, contrarily, their stage remains same. The proposed approach uses two types of features i.e. volumetric features and textural features. Volumetric features consist of volume of grey matter, volume of white matter and volume of cerebrospinal fluid. A total of 364 textural features have been calculated. To avoid the curse of dimensionality, textural features are reduced to 15 features using gain ratio, a ranking based search algorithm. All features are tested against four classifiers i.e. AODEsr, VFI, RBF and LBR. Leave-One-Out cross validation strategy is used for the evaluation of proposed approach. Results show accuracy of 98.33% with volumetric features and 100% with textural features using VFI and LBR. Our approach is innovative because of its higher accuracy results as compared to existing approaches yet with a smaller feature set.


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References Weight

Web of Science® Citations for all references: 28,441 TCR
SCOPUS® Citations for all references: 29,716 TCR

Web of Science® Average Citations per reference: 537 ACR
SCOPUS® Average Citations per reference: 561 ACR

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

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