Description

A dynamic dual-graph fusion convolutional network is suggested in this paper to enhance the accuracy of Alzheimer’s disease (AD) diagnosis. The key contributions of the paper are as follows: The proposed architecture can dynamically adjust the graph structure for GCN to produce better diagnosis outcomes by learning the optimal underlying latent graph, propose a novel dynamic GCN architecture, which is an end-to-end pipeline for diagnosing AD, incorporate feature graph learning and dynamic graph learning, giving those useful features of subjects more weight while reducing the weights of other noise features. Experiments show that our approach achieves great classification results in AD diagnosis while offering flexibility and stability. This project is implemented for the Alzheimer disease classification using Graph convolutional network architecture like CNN with 2D layer and fit the model in the training and testing and deploy the model for getting the test image output and then compare the accuracy of the model for getting the most perfect model for medical image classification and get the accuracy of CNN- 2D layer with 98.75%.