liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Automatic Detection of Alzheimer Disease Based on Histogram and Random Forest
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Effat Univ, Saudi Arabia.
2020 (English)In: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING, CMBEBIH 2019, SPRINGER , 2020, Vol. 73, p. 91-96Conference paper, Published paper (Refereed)
Abstract [en]

Alzheimer disease is one of the most prevalent dementia types affecting elder population. On-time detection of the Alzheimer disease (AD) is valuable for finding new approaches for the AD treatment. Our primary interest lies in obtaining a reliable, but simple and fast model for automatic AD detection. The approach we introduced in the present contribution to identify AD is based on the application of machine learning (ML) techniques. For the first step, we use histogram to transform brain images to feature vectors, containing the relevant "brain" features, which will later serve as the inputs in the classification step. Next, we use the ML algorithms in the classification task to identify AD. The model presented and elaborated in the present contribution demonstrated satisfactory performances. Experimental results suggested that the Random Forest classifier can discriminate the AD subjects from the control subjects. The presented modeling approach, consisting of the histogram as the feature extractor and Random Forest as the classifier, yielded to the sufficiently high overall accuracy rate of 85.77%.

Place, publisher, year, edition, pages
SPRINGER , 2020. Vol. 73, p. 91-96
Series
IFMBE Proceedings, ISSN 1680-0737
Keywords [en]
Alzheimer disease; Histogram; Random forest classifier
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-161583DOI: 10.1007/978-3-030-17971-7_14ISI: 000491311000014ISBN: 978-3-030-17971-7 (electronic)ISBN: 978-3-030-17970-0 (print)OAI: oai:DiVA.org:liu-161583DiVA, id: diva2:1368248
Conference
International Conference on Medical and Biological Engineering in Bosnia and Herzegovina (CMBEBIH)
Available from: 2019-11-06 Created: 2019-11-06 Last updated: 2019-11-06

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Alickovic, Emina
By organisation
Automatic ControlFaculty of Science & Engineering
Medical Image Processing

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 43 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf