Abstract
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.
Highlights
Some of the existing systems were studied while reading different papers on systems used to Detect Alzheimer's Disease from MRI scans
Sparse filtering was done in the first stage to learn the features of all MRI scans of the brain which were obtained from Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset
Convolutional Neural Networks (CNNs) is a category of deep neural network which has proven very effective in areas such as image recognition and classification
Summary
Though there isn’t any cure for the disease, an early detection with effective medication can help keep the brain cells active, making a person’s life a little better. The aim of this project is to develop a system for detecting Alzheimer’s disease in its early stage. It will be using a deep learning model for classification of patients into different categories. Step is training CNN model with these preprocessed images and the last step is classification
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