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Abstract
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Article Information:
Alzheimer’s Disease Classification Using Hybrid Neuro Fuzzy Runge-Kutta (HNFRK) Classifier
R. Sampath and A. Saradha
Corresponding Author: R. Sampath
Submitted: November 10, 2014
Accepted: January 21, 2015
Published: May 10, 2015 |
Abstract:
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Alzheimer’s Disease (AD) exists more prior to the over appearance of clinical symptoms and is characterized by brain changes. In this study, Functional Magnetic Resonance Imaging (FMRI) offers considerable promise as a tool for detecting brain changes in Alzheimer disease pretentious patients. Therefore, FMRI may offer the unique ability to detention of the dynamic state of change in the collapsing brain. Improve the accuracy of brain FMRI image segmentation, a robust Spatial Fuzzy C-Means (SFCM) is utilized and a combination of Adaptive Neuro Fuzzy Inference System and Runge-Kutta Learning Algorithm called Hybrid Neuro Fuzzy Runge-Kutta (HNFRK) classifier is used for prediction of Alzheimer’s Disease (AD). The performance of the proposed classifier is compared with SVM and ANFIS classifier. The results show that the sensitivity and specificity of HNFRK classifier is more compared to the SVM and ANFIS. The sensitivity and specificity of HNFRK is above 90% which is below 90% in case of SVM and ANFIS classifier. Thus it can be shown that HNFRK performs accurate classification than SVM and ANFIS.
Key words: Alzheimer’s Disease (AD), ANFIS (Adaptive Neuro Fuzzy Inference System), FMRI images, Runge-Kutta Learning algorithm (RKLM), Spatial Fuzzy C-Means (SFCM), ,
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Abstract
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Cite this Reference:
R. Sampath and A. Saradha, . Alzheimer’s Disease Classification Using Hybrid Neuro Fuzzy Runge-Kutta (HNFRK) Classifier. Research Journal of Applied Sciences, Engineering and Technology, (1): 29-34.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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