An Extensive Study of Alzheimer’s Disease Detection Using Deep Learning

There is a significant cause for concern in contemporary medical treatment regarding Alzheimer’s disease (AD), which is the greatest mutual form of dementia. AD is a brain ailment that is permanent and degenerative, and it is defined by an injury of cognitive function. There is currently no known treatment for this condition. Over the past uncommon years, there has been a discernible rise in the sum of initiatives working towards the diagnosis of Alzheimer’s disease and the treatment of its development. The development of this illness has been shown to be significantly influenced by a number of variables, including genetic and environmental factors, as well as stress and diet. The use of computer-aided investigation models that are founded on artificial intelligence has the potential to greatly boost the investigation of a variety of neuroimaging techniques and non-image biomarkers. Deep learning approaches have seen a surge in popularity over the past several years, notably in the fields of computer vision and natural language processing. Beginning in 2014, these methodologies have started to receive significant consideration in the field of Alzheimer’s disease (AD) diagnostic research, and the number of articles that have been published in this field is significantly increasing. Studies reveal that deep learning techniques are more accurate in diagnosing Alzheimer’s disease. This article evaluates the present state of the art in Alzheimer’s disease diagnosis using deep learning. Using an inclusive evaluation of the relevant literature, the research provides a summary of the most recent findings and trends. The employment that is now under examination has been classified and clarified in order to show the underlying issues. Despite the fact that deep learning has demonstrated some potential in the detection of (AD), there are still a sum of obstacles that need to be overwhelmed. In the conclusion portion of our work, we provide recommendations for future research exertions on the judgement of AD, as well as a discussion of potential pathways for additional exploration.