Classifying Alzheimer’s Disease Using Adaptive Neuro Fuzzy Inference System
D.S. Gayathri1, Nagarajan Munusamy2

1D.S. Gayathri, Research Scholar, Department of Computer Science, Bharathiyar University, India.
2Dr. Nagarajan Munusamy, Associate Professor, Department of Computer Science, CMS College of Science & Commerce, Coimbatore (Tamil Nadu), India.
Manuscript received on 14 December 2018 | Revised Manuscript received on 26 December 2018 | Manuscript Published on 24 January 2019 | PP: 227-233 | Volume-7 Issue-4S2 December 2018 | Retrieval Number: Es2062017519/19©BEIESP
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Abstract: Alzheimer’s Disease (AD) may be a sort of dementia disease which is unpredictable with diagnose by understanding the clinical perception alone. Identifying Alzheimer’sdiseaseby scanning the brain using Magnetic Resonance Imaging (MRI) information is a Fundamental concern in the neurosciences. Universal evaluation of functional scan images regularly depends on Manual reorientation, visual reading and Furthermore, semi quantitative examination from certain specific segments of the cerebrum.This paper suggested the Adaptive Neuro methodology for robotized multiclass analysis of Dementia with the higher order reasoning about MRI Image of a human Brain. The Process begins with the pre-processing the MRI Images by disposing the noises present in them, like labels and X- Ray marks by using Tracking Algorithm. Feature Extraction process, eliminates the high frequency components using Discrete Wavelet Transform (DWT). Thus derived coefficients makeuse of primary couple of DWT coefficients for the preparation of classification in the means of Normal, Mild cognitive Influence; Alzheimer’s disease using Adaptive Neuro Fuzzy Algorithm (ANFIS). The testresult consequence demonstrates that the proposed technique execution posses a better result than by comparing with different order methodologies.
Keywords: ANFIS, MRI Image, Alzheimer’s Disease, DWT.
Scope of the Article: Fuzzy Logics