Alzheimers disease course prediction methods based on deep learning methods

Abstract

Alzheimer 's disease (AD) is a primary disease of the nervous system with memory and cognitive impairment as the main symptoms. It is the most common type of senile dementia and seriously threatens the quality of life and life safety of the elderly. Due to the pathological characteristics of AD and the limitations of medical technology, there is currently no very effective treatment worldwide. Therefore, early prediction and diagnosis of AD has always been a research hotspot worldwide. Deep learning may finish the joint classification of several stages by mining the abundant information implicit in the visual data collected from patients. It is a hub for computer-assisted diagnosis research. Therefore, many studies have applied computer-aided methods such as deep learning to the medical image data of Alzheimer 's disease, so as to provide ideas and help for prediction and diagnosis of diseases. This paper analyzes and compares various network structures, summarizes the use of the current, comparatively new deep learning network models applied in the classification of Alzheimer's disease course, and discusses the difficulties and potential future research directions for deep learning in the field of AD diagnosis. Thus, it is very important to raise the efficiency of the clinical diagnosis of AD and the accuracy of early forecasts.