Application of fuzzy logic for Alzheimer's disease diagnosis

Igor Krashenyi,Anton Popov,Javier Ramirez,Gorriz, J.M.

Signal Processing Symposium（2015）

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摘要

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).

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关键词

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,MRI,classification,fuzzy logic,mild-cognitive impairment

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