DETECTION OF ALZHEIMER’S DISEASE

Abstract

Alzheimer's disease is a progressive brain condition that cannot be reversed. Someone in the world is diagnosed with Alzheimer's disease every four seconds. It is a disease of the brain that worsens with age. Alzheimer's disease is the leading contributor to dementia. Because it reduces a person's capacity for reasoning and interpersonal coping skills, dementia affects their ability to live independently. The patient won't be able to recall details until it is too late in the course of their illness. They will eventually forget entire events as the illness progresses. Because Promotion is a disease that changes all the time, early detection and organization can greatly aid in infectious prevention. Recent studies have utilized voxel- based brain MR image feature extraction methods and machine learning algorithms for this purpose. Since Advancement impacts and damages the white and faint matter of the frontal cortex, focusing on these two areas turns out to be all the more remarkable at expecting the infection. It is essential to eradicate the disease as soon as possible. A model that takes sample MRI images of the brain as input and determines whether a person has Alzheimer's disease as an output is the subject of this project. For this request, we are differentiating the VGG19 and DenseNet169 structures with sort out which one offers promising outcomes.