PANN: An Efficient Parallel Neural Network based on the Attentional Mechanism for Predicting Alzheimer's Disease

In recent years, machine learning methods have been extensively studied in Alzheimer's disease (AD) prediction. Most existing methods extract the handcraft features from images and then train a classifier for prediction. Although it has good performance, it has some deficiencies in essence, such as relying too much on image preprocessing, easily ignoring the latent lesion features. This paper proposes a deep learning network model based on the attention mechanism to learn the latent features of PET images for AD prediction. Firstly, we design a novel backbone network based on ResNet18 to capture the potential features of the lesion and avoid the problems of gradient disappearance and gradient explosion. Secondly, we add the channel attention mechanism so that the model can learn to use global information to selectively emphasize information features and suppress low-value features, which is conducive to the extraction of semantic features. Finally, we expand the data by horizontal flipping and random flipping, which reduces the over-fitting phenomenon caused by the limited medical data set and improves the generalization ability of the model. This method is evaluated on 238 brain PET images collected in the ADNI database, and the prediction accuracy is 94.2%, which is better than most mainstream algorithms.