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Review on Local Feature Descriptors for Early Detection of Alzheimer’s Disease

Alzheimer’s disease (AD) is a brain disorder that affects the nerve cells. Gradually the patient is not able to do the daily routines and activities. Medical experts can not completely cure this disease. But if this disease is diagnosing as much as early, then they can start some medicines to reduce rate of brain damage. Also, the patient and his relatives can be prepared themselves. Hence early detection of Alzheimer’s disease is one of the major problems faced by the medical field. The four stages of Cognitive state of a human brain can be classified as Alzheimer’s disease (AD), Mild Cognitive Impairment Convertible (MCIc), Mild Cognitive Impairment Non-Convertible (MCInc) and Cognitive Normal (CN). MCIc is a stage which can be lead to AD in few years. MCInc is a stage in which cognitive abnormalities will be present, but it will not lead to AD. In the context of early detection of AD, MCIc vs MCInc classification is decisive. In this work, feature descriptors used for the classification of above four stages were studied with MRI Images. We emphasized the performance of different local feature descriptors. After the review, concluded that classification accuracy between MCIc vs MCInc should be improved for the early detection of AD