Automatic classification of Alzheimer's disease using DenseNet model
In order to study the accuracy of deep learning algorithms in classifying Alzheimer's disease,a dense convolutional neural network(DenseNet)method was proposed.The preprocessed data is used to train a dense convolutional neural network structure and classify Alzheimer's disease and people with normal cogni-tion.The test results show that the classification accuracy obtained by this method is 98.91%.The accuracy rate of classifying Alzheimer's disease and mild cognitive impairment is 94.54%,which is significantly im-proved over other algorithms and provides an effective solution for the accurate classification of Alzheimer's disease.
Alzheimer's diseasebrain magnetic resonance imaging imagesdeep learningdense network
NETL
NSTL
万方数据
