With the intensified aging problem, the study of age-related diseases is becoming more and more significant. Alzheimer's disease is a kind of dementia, with senile plaques and neurofibrillary tangles as the main pathological features, and has become one of the major diseases that endanger the health of the elderly. This review is concentrated on the research of the early assessment of Alzheimer's disease. The current situation of early diagnosis of the disease is analyzed, and a prospect of the future development of early assessment means of the disease is also made in the paper.
With the wide application of deep learning technology in disease diagnosis, especially the outstanding performance of convolutional neural network (CNN) in computer vision and image processing, more and more studies have proposed to use this algorithm to achieve the classification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal cognition (CN). This article systematically reviews the application progress of several classic convolutional neural network models in brain image analysis and diagnosis at different stages of Alzheimer’s disease, and discusses the existing problems and gives the possible development directions in order to provide some references.
