Alzheimer's Disease Classification Using Hybrid Neuro Fuzzy Runge-Kutta (HNFRK) Classifier

R. Sampath ； A. Saradha

《Research Journal of Applied Sciences, Engineering and Technology》 10卷1期 (2015/05) Pp. 29-34

並列摘要

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.

並列關鍵字

Alzheimer's Disease (AD) ； ANFIS (Adaptive Neuro Fuzzy Inference System) ； FMRI images ； Runge- Kutta Learning algorithm (RKLM) ； Spatial Fuzzy C-Means (SFCM)