A Review of Alzhemeir’s Disease Detection

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by progressive memory loss and cognitive decline. There is currently no cure for AD, but early diagnosis and treatment can help to slow the progression of the disease. Deep learning is a type of machine learning that has been shown to be effective in a variety of tasks, including image classification, natural language processing, and speech recognition. In recent years, deep learning has also been used for the early detection of AD. One approach to using deep learning for AD detection is to train a deep neural network to classify MRI images of the brain. MRI images can be used to identify structural changes in the brain that are associated with AD, such as atrophy of the hippocampus and the amygdala. Deep neural networks can be trained to identify these changes with high accuracy. Another approach to using deep learning for AD detection is to train a deep neural network to predict the levels of AD biomarkers in the blood. AD biomarkers are proteins that are found in the blood of people with AD. Deep neural networks can be trained to predict the levels of these biomarkers with high accuracy. Deep learning is a promising new tool for the early detection of AD. Deep learning algorithms can be trained to identify subtle changes in the brain and blood that are associated with AD. This early detection can help to improve the chances of successful treatment.