ISSN: 2349-5162 | Impact Factor: 5.87

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Published in:

Volume 5 Issue 9
September-2018
eISSN: 2349-5162

Unique Identifier

JETIRB006068

Page Number

371-377

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Title

An Improved Alzhemeir’s Disease Detection Using Improved Unsupervised Mapping Technique

ISSN

2349-5162

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"An Improved Alzhemeir’s Disease Detection Using Improved Unsupervised Mapping Technique ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 9, page no.371-377, September-2018, Available :http://www.jetir.org/papers/JETIRB006068.pdf

Abstract

The Alzheimer’s disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third,just behind heart disease and cancer, as a cause of death for older people. So, predicting in its early stage may prevent it from progressing and may be of great importance. The diagnosis of Alzheimer’s disease (AD) requires a range of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient computational approach for accurate prediction of the changes in the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient mapping technique for differentiating affected MRI images from normal .This employs unsupervised learning framework with improved adaptive moving self organizing mapping(AMSOM) method. We first apply median filter for denoising and then feature extraction to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The 24 features are captured with most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a clustering over the set of vectors for classifying and clustering faster. To use the best extracted features, we present a hybrid learning framework which embeds the feature vectors in a subspace. Next, using a small set of subjects, we apply clustering method in the created space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer’s and normal condition (MCI/NC).

Key Words

Adaptive moving fuzzy self organize mapping, clustering, Feature extraction, Self organisedmapping(SOM) .

Cite This Article

"An Improved Alzhemeir’s Disease Detection Using Improved Unsupervised Mapping Technique ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 9, page no. pp371-377, September-2018, Available at : http://www.jetir.org/papers/JETIRB006068.pdf

Publication Details

Published Paper ID: JETIRB006068
Registration ID: 186313
Published In: Volume 5 | Issue 9 | Year September-2018
DOI (Digital Object Identifier):
Page No: 371-377
ISSN Number: 2349-5162

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(Volume 5 | Issue 9 | September 2018 |Impact factor 5.87)

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Cite This Article

"An Improved Alzhemeir’s Disease Detection Using Improved Unsupervised Mapping Technique ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 9, page no. pp371-377, September-2018, Available at : http://www.jetir.org/papers/JETIRB006068.pdf

Important Dates Related to Publication Procedure:
  • » Paper Submission Till: 29 September 2018.
  • » UGC and ISSN Approved Journal
  • » Review (Acceptance/Rejection) Notification: Within 02-04 Days.
  • » Paper Publish:Within 02-07 Days after submitting the all documents.
  • » Frequency: Monthly (12 issue Annually)
  • » Journal Type : Open Access
  • »Indexing In Google Scholar, ResearcherID Thomson Reuters, Mendeley : reference manager, Academia.edu, arXiv.org, Research Gate, CiteSeerX, DocStoc, ISSUU, Scribd, and many more | High Impact Factor: 5.87 Digital object identifier (DOI) and Hard Copy of certificate Provided.
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Impact factor: 5.87