Alzheimer’s disease diagnosis via 5-layer Convolutional Neural Network and Data Augmentation

Abstract

OBJECTIVES: Alzheimer's disease (AD) is a progressive neurodegenerative disease with insidious onset and one of the biggest challenges in geriatrics. Because the cause of the disease is unknown and there is currently no cure, AD Early diagnosis is particularly important. METHODS: In this paper, we built a 5-layer convolutional neural network based on deep learning technology. We used six data augmentation methods to increase the size of the training set. Batch normalization and dropout techniques are also used, which are respectively associated with the convolutional layer and the fully connected layer, Form convolution batch normalization (CB) and dropout fully connected (DOFC) block respectively. RESULTS: Our 5-layer CNN has shown excellent results on the training set, a sensitivity of 94.80%, a specificity of 93.98%, a precision of 94.04% and an accuracy of 94.39%, and has good performance compared with several other state-of-the-art methods. CONCLUSION: In terms of classification performance, our method performs better than 8 state-of-the-art approaches and the performance of human observers. Therefore, this proposed method is effective in the detection of Alzheimer's disease.