Application of fuzzy logic for Alzheimer´s disease diagnosis

Author

Krashenyi, Igor ; Popov, Anton ; Ramirez, Javier ; Gorriz, Juan Manuel

Author_Institution

Phys. & Biomed. Electron. Dept., Nat. Tech. Univ. of Ukraine, Kiev, Ukraine

fYear

2015

fDate

10-12 June 2015

Firstpage

1

Lastpage

4

Abstract

Fuzzy Inference System (FIS) is developed using subtractive clustering algorithm, and applied to classification between MRI images of patients having Mild Cognitive Impairment (MCI) or Alzheimer´s Disease (AD) and Normal Controls (NC). Features used as FIS inputs are mean values and standard deviations in intensities from most descriptive brain regions. k-fold cross-validation was used to estimate FIS performance, resulting in accuracy, sensitivity, specificity and positive predictive value (ppv) characteristics of FIS classification between different groups. ppv was equal to 0.8778±0.0088 (AD vs. NC), 0.7289±0.0243 (NC vs. MCI), and 0.8531±0.0069 (MCI vs. AD).

Keywords

biomedical MRI; brain; diseases; fuzzy logic; fuzzy reasoning; medical image processing; pattern clustering; Alzheimer´s disease diagnosis; FIS classification; FIS performance; MCI; MRI images; brain region; fuzzy inference system; fuzzy logic; k-fold cross-validation; mild cognitive impairment; normal control; subtractive clustering algorithm; Alzheimer´s disease; Databases; Fuzzy logic; Magnetic resonance imaging; Standards; Support vector machines; Alzheimer´s disease; MRI; classification; fuzzy logic; mild-cognitive impairment;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Symposium (SPSympo), 2015

Conference_Location

Debe

Type

conf

DOI

10.1109/SPS.2015.7168288

Filename

7168288