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Alzheimer's disease is a progressive neurological brain disorder that gradually destroys memory cells and causes the brain to shrink. It is the most common cause of Dementia, which results in a steady decline in cognitive skills such as thinking, social, and behavioral skills, affecting the person's ability to work independently. This disease is usually found in theage group of 65 years and above. Alzheimer's disease must be detected early in order to prevent the disease from progressing irreversibly. In this work, an approach for detecting Alzheimer's disease in its early stages is proposed. The proposed method employs a Machine Learning algorithm to diagnose Alzheimer's disease from an MRI scan of the Hippocampus. The Hippocampus region's area, shape, and texture features are retrieved, and the percentage and stages of Alzheimer's Disease are detected using these features. For feature extraction and feature classification, Machine Learning algorithms such as Convolutional Neural Network and K-Nearest Neighbor are utilised. In comparison to other conventional methodologies, the proposed approach is predicted to provide improved accuracy |
