Application of Deep Learning and Brain Images in Diagnosis of Alzheimer’s Patients

Abstract

In the identification of which stages Alzheimer’s patients are in, the application of the medical imaging technology helps doctors give more accurate qualitative diagnoses. However, the existing research results are not effective enough in the acquisition of valuable information from medical images, nor can they make full use of other modal images that highlight different feature information. To this end, this paper studies the application of deep learning and brain images in the diagnosis of Alzheimer’s patients. First, the image preprocessing operations and the brain image registration process were explained in detail. Then, the image block generation process was given, and the degrees of membership to white matter, gray matter and cerebrospinal fluid were calculated, and the brain images were also preliminarily classified. Finally, a complete auxiliary diagnosis process for Alzheimer’s disease based on deep learning was provided, an improved sparse noise reduction auto-encoder network was constructed, and the brain image recognition and classification based on deep learning were completed. The experimental results verified the effectiveness of the constructed model.

Leveraging Deep Learning for Enhanced Detection of Alzheimer’s Disease Through Morphometric Analysis of Brain Images

“…CNN is a neural network consisting of several layers, which is used in the field of image processing [33]. There are intensive studies on CNN based medical image processing [34,35]. Compared to other DL techniques, CNN is preferred for disease diagnosis through neuroimaging, as it specializes in learning images.…”