Proposed System for Detection of Alzheimer’s Disease

Siddhi Raajjhesh Bothraa, Jonathan Bob Jackson, Shruti Telang


Alzheimer's disease is a neurological disorder in which the death of brain cells causes memory loss and cognitive decline. It is a type of dementia that gradually destroys brain cells, affecting a person's memory. It is an irreversible, progressive brain disorder that slowly destroys memory, thinking skills and the ability to carry out the simplest tasks. Alzheimer's disease is the most common cause of dementia among older adults. Pre-detection is crucial for such a disease as drugs will be most effective if administered early in the course of the disease. If not done on time, it can lead to irreversible brain damage. Therefore, it is very important to utilize automated techniques for pre-detection of Alzheimer's symptoms from such data. The system uses an experimental approach to evaluate the best pre-detection method of Alzheimer’s disease. The study consists of two parts. First is obtaining the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset and performing Image Processing on it which will be used to train the system. Next is using a Deep Learning algorithm to detect the disease from this neuroimaging data.


ADNI, Alzheimer’s Disease, Convolution Neural Networks, Deep Learning, Image Processing

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