Abstract
Alzheimer's disease is a degenerative neurological disorder that primarily strikes older people. Alzheimer's disease is now a major health problem for anyone over the age of 65. The inability to remember what has been said or done before is the first symptom of the condition. Memory loss becomes severe, and daily functioning declines as the disease progresses. The memory-controlling region of the brain shows signs of impairment years before any symptoms occur. There are three possible disease stages: mild, moderate, and severe. The early stage, sometimes known as the middle stage, mild demented (MCI), is an intermediate state between Alzheimer’s patients and healthy individuals When someone is diagnosed with MCI, there is an opportunity to treat or stop the development of the disease into AD, which is the only solution to avoid AD. Therefore, the early detection of AD plays a crucial role in preventing and controlling its progression. The main objective is to design an end-to-end framework for The Early detection of Alzheimer’s disease and medical image classification for various AD stages. A deep learning approach, specifically convolutional neural networks (CNN), is used in this work. Based on MRI scans, The four phases of Alzheimer's disease are correctly categorized by the suggested technique with an accuracy of 95.17 % performance, 86.82 % precision, and 93.13 f1 score.
