Neuro-cogniguard: a Deep Learning Approach for Early Alzheimer's Detection

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and memory impairment. This disease causes the person to suffer from memory loss, unusual behaviour, and language problems. Initial symptoms such as episodic memory impairment and patient navigation problems are typical variants. In this project, we propose a deep learning method for early and accurate detection of AD using MRI images. In the existing system, they used deep learning models such as Convolutional Neural Networks (CNN) and LeNet5 architecture to detect the disease. In the proposed system, we use The Deep Learning Model as the cellular network architecture and classify images as mild dementia, moderate dementia, non-demented, or very mild dementia. In this study, we use the ADNI dataset. Using these algorithms can achieve better accuracy for CNN and Mobile Net compared to the existing system. This can be used in the future to classify the types of different classifications that can be easily detected in the initial stages and can only be cured in the initial stages.