Convolutional neural network based on feature enhancement and attention mechanism for Alzheimer's disease prediction using MRI images

Abstract

Nuclear Magnetic Resonance Imaging(MRI) is the mainstream way to predict Alzheimer's disease, but the accuracy of traditional machine learning method based on MRI to predict Alzheimer's disease is low. Although Convolutional Neural Network(CNN) can automatically extract image features, convolution operations only focus on local regions and lose global connections. The attention mechanism can focus on local and global information at the same time, and improve the performance of the model by strengthening the key information to suppress invalid information.Therefore, this paper constructs a deep CNN based on multiple attention mechanisms for Alzheimer's disease prediction. Firstly, the MRI image is enhanced by cyclic convolution to enhance the feature information of the original image, so as to improve the prediction accuracy and stability. Secondly, multiple attention mechanisms are introduced to re-calibrate features and adaptively learn feature weights to identify brain regions that are particularly relevant for disease diagnosis. Finally, an improved VGG model is proposed as the backbone network. The maximum pooling is adjusted to average pooling to retain more image information and the network efficiency is improved by reducing the number of neurons in the fully connected layer to suppress over-fitting merging. The experimental results show that the prediction accuracy, sensitivity and specificity of Alzheimer's disease prediction method based on multiple attention mechanism are 99.8%, 99.9% and 99.8%, respectively, which is superior to the existing mainstream methods.