An Automatic Encoding and Decoding Method for Differentiating Alzheimer's Disease with Functional MRI

Abstract

In recent years, promising performance of classifying the Alzermerzer's Disease has been achieved by using functional resting-state MRI to extract features by functional connectivity and brain activation in different brain regions such as ReHO, ALFF and so on. However current studies focus on the feature extraction by analyzing the whole time series extracted from the functional images, without considering the variation of the signature changes in the brain regions, which might cause fluctuations of the brain signature activation or the analysis of functional connectivity. This study focus on the image feature automatic encoding and decoding in sequence by a network, where convolutional neural network is used to extract abstract image features in each time step and a long-short term recurrent neural network used to combine features at all time. And finally we use the network to carry out experiments to identify the Alzermerzer's Disease. Our CNN network is developed from the U-net, where we only use the first half of the network to encode the images. Finally we have gained a considerable accuracy in average.