Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder which connected to the progression of declining in memory and thinking skills. A more accurate diagnosis and appropriate management are needed to determine correct treatments. Human brain imagery using Magnetic Resonance Imaging (MRI) has different kinds of brain tissue: Grey-Matter (GM) and White Matter (WM), and image of colorless body fluid which called Cerebrospinal Fluid (CSF). This article discusses the use of GM, WM and CSF imagery and the proportion values of the volume of each image in determining Alzheimer's identification. Our study is using 3different anatomical plane image acquisition of MRI and using feature extraction based on Kolmogorov-Smirnov distance. Supervised Neural Network Backpropagation was used for classifier machine. The results of testing on every combination of the different anatomical plane of MRI images we finally proposed that individual WM or GM analyses are a better compared to the scenario of combining between GM, WM, and CSF.

Original languageEnglish
Title of host publication2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages285-288
Number of pages4
ISBN (Electronic)9781538675090
DOIs
Publication statusPublished - 2 Jul 2018
Event2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Surabaya, Indonesia
Duration: 26 Nov 201827 Nov 2018

Publication series

Name2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding

Conference

Conference2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018
Country/TerritoryIndonesia
CitySurabaya
Period26/11/1827/11/18

Keywords

  • Alzheimer's
  • Kolmogorov-Smirnov distance
  • cerebrospinal fluid
  • grey matter
  • neural network backpropagation
  • white matter

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