Alzheimer disease diagnosis using the K-means, GLCM and K-NN

Author

Ulaywi, Wid Kazim

Source

Journal of Babylon University : Journal of Applied and Pure Sciences

Issue

Vol. 26, Issue 2 (28 Feb. 2018), pp.57-65, 9 p.

Publisher

University of Babylon

Publication Date

2018-02-28

Country of Publication

Iraq

No. of Pages

9

Main Subjects

Mathematics

Abstract EN

Investigation of medical images have major consequence in the field of treatment.in this work ,MR images have been used to distinguish the normal brain from brain with Alzheimer disease .Texture is an native property of all surfaces it contains important facts about the structural organization of the surfaces and their connections neighboring area.

In direction to classify texture must be segmented into a number of section that has the similar properties, for this purpose we used k- means algorithm with GLCM for feature extraction ,finally we used k-nearest neighbor algorithm to distinguish between normal and abnormal brain.

American Psychological Association (APA)

Ulaywi, Wid Kazim. 2018. Alzheimer disease diagnosis using the K-means, GLCM and K-NN. Journal of Babylon University : Journal of Applied and Pure Sciences،Vol. 26, no. 2, pp.57-65.
https://search.emarefa.net/detail/BIM-1094077

Modern Language Association (MLA)

Ulaywi, Wid Kazim. Alzheimer disease diagnosis using the K-means, GLCM and K-NN. Journal of Babylon University : Journal of Applied and Pure Sciences Vol. 26, no. 2 (2018), pp.57-65.
https://search.emarefa.net/detail/BIM-1094077

American Medical Association (AMA)

Ulaywi, Wid Kazim. Alzheimer disease diagnosis using the K-means, GLCM and K-NN. Journal of Babylon University : Journal of Applied and Pure Sciences. 2018. Vol. 26, no. 2, pp.57-65.
https://search.emarefa.net/detail/BIM-1094077

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references : p. 64-65

Record ID

BIM-1094077