Early Stage Detection of Alzheimer's using Hybrid Artificial Intelligence Model: A Review

The most well-known form of dementia, AD - Alzheimer's Disease, is a critical problem in contemporary medicine. AD affects over 5.8 million individuals aged 65 and older, causing the sixth greatest reason for death in the US. A loss of cognitive function is the hallmark of AD, a degenerative brain condition that is irreversible and for which there is no known therapy. Deep learning approaches have been increasingly popular recently, especially in natural language processing and computer vision. These methods have started to get significant in research on Alzheimer's Disease diagnosis, and the volume of publications published in this area has been rapidly increasing since 2014. According to reports, deep learning methods are superior to traditional machine learning models for diagnosing AD. This paper evaluates the present state-of-the-art in Alzheimer's Disease diagnosis using Deep Learning to examine the potential of Deep Learning in Alzheimer's Disease diagnosis. Moreover, highlight the current trends and findings using a comprehensive literature analysis. The study investigates several datasets and biomarkers for diagnosing AD. Despite the potential that Deep Learning has shown in diagnosing AD, several issues still need to be resolved.