Image Classification Detection Technology in the Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is a primary brain degenerative disease that occurs in the elderly and pre - elderly. This disease will produce irreversible brain structural and molecular changes, leading to progressive cognitive and behavioral disorders. The disease has the characteristics of strong concealment and difficult early diagnosis, which also leads to a very difficult diagnosis. Therefore, with the rise of deep learning, researchers began to use image classification detection technology to assist in the diagnosis of Alzheimer's disease. This paper summarizes the application of image classification and detection technology in the diagnosis of Alzheimer's disease from three parts: the application of the single convolutional neural network method in diagnosis, the application of the attention mechanism and the convolutional neural network fusion method in diagnosis, and the application of the fusion model method in diagnosis. At the same time, this paper also makes a comparative analysis of the mainstream related databases, including data scale, queue diversity, data mode, research scenarios, and acquisition methods. The research in this paper not only provides the basis for researchers to select models and data, but also promotes the integration of image technology and Alzheimer's disease diagnosis.