The Online Datasets Used to Classify the Different Stages for the Early Diagnosis of Alzheimer’s Disease (AD)
Sandeep C. S1, Sukesh Kumar A2, Susanth M. J.3

1Sandeep C. S, Research Scholar, Faculty of Engineering, University of Kerala, Trivandrum, India.
2Sukesh Kumar A, Research Guide, Faculty of Engineering, University of Kerala, Trivandrum, India.
3Susanth M J, Consultant Neurologist, SUT Hospital, Trivandrum, India.

Manuscript received on 13 April 2017 | Revised Manuscript received on 20 April 2017 | Manuscript Published on 30 April 2017 | PP: 38-45 | Volume-6 Issue-4, April 2017 | Retrieval Number: D4883046417/17©BEIESP
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Abstract: Alzheimer Disease (AD) is one of the common forms of dementia which is an irreversible neurodegenerative progressive disorder of the brain which affects the elderly population above the age of 65. Alzheimer is a brain disease that causes problems with memory, thinking and behavior. It is severe enough to interfere with daily activities. Alzheimer symptoms are characterized by memory loss that affects day-to-day function, difficulty performing familiar tasks, problems with language, disorientation of time and place, poor or decreased judgment, problems with abstract thinking, misplacing things, changes in mood and behavior, changes in personality and loss of initiative. There are different types of tests associated with AD such as neuropsychological tests, laboratory tests and various imaging modalities for the early diagnosis of AD. Although these tests are available, they are inadequate for the definite diagnosis of the disease. In this paper we focus on the databases related to AD such as ADNI (Alzheimer Disease Neuroimaging Initiative), OASIS (Open Access Series of Imaging studies), Alz Gene, AD&FTDMDB (The Alzheimer Disease & Frontotemporal Dementia Mutation Database), (CAMD) Alzheimer’s disease Database and NAAC( National Alzheimer’s Coordinating Center), TREAD (Trajectory-Related Early Alzheimer’s Database), Coalition Against Major Diseases use of the soft computing techniques and image analysis from the different imaging modalities in an efficient way for making a definite diagnosis and early prediction of AD. Our aim is to predict the early diagnosis in a reliable manner such that to combine the values of different tests with the help of soft computing techniques to develop software tool for a definite diagnosis.
Keywords: Alzheimer Disease, Dementia, ADNI, OASIS, AlzGene, AD&FTDMDB, TREAD, NAAC. Soft Computing Techniques, Image Analysis

Scope of the Article: Could Computing