Fuzzy clustering methods application for Alzheimer’s diseases diagnosis based on PET images

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Published: Oct 27, 2016
DOI: https://doi.org/10.20535/2312-1807.2016.21.2.51681
Keywords:
Alzheimer's disaese, dementia, fuzzy logic, positron emission tomography

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Ihor Eduardovych Krashenyi
National technical university of Ukraine "Igor Sikorsky Kyiv polytechnic institute"
Anton Oleksandrovych Popov
National technical university of Ukraine "Igor Sikorsky Kyiv polytechnic institute"
Haver Ramirez
University of Granada
Huan Manuel Gorriz
University of Granada

Abstract

This work was dedicated to clustering methods application in fuzzy inference system for Alzheimer’s disease diagnosis using PET-images. Three methods (Subtractive Clustering, C-means and Fuzzy Grid Partition) of clustering were discussed and their performance in Alzheimer’s disease diagnosis were measured. Recommendation of the future use of Subtractive Clustering algorithm in the computeraided diagnosis system for Alzheimer’s disease are given. The performance of this algorithm is AUC=0,8791.

Ref. 20, fig. 3, tab. 3.

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How to Cite
Krashenyi, I. E., Popov, A. O., Ramirez, H., & Gorriz, H. M. (2016). Fuzzy clustering methods application for Alzheimer’s diseases diagnosis based on PET images. Electronics and Communications, 21(2), 56–62. https://doi.org/10.20535/2312-1807.2016.21.2.51681
Issue
Vol. 21 No. 2 (2016)
Section
Biomedical devices and systems
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