Classification of Alzheimer’s Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling

Shui Hua Wang*, Preetha Phillips*, Yuxiu Sui, Bin Liu, Ming Yang, Hong Cheng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

267 Citations (Scopus)

Abstract

Alzheimer’s disease (AD) is a progressive brain disease. The goal of this study is to provide a new computer-vision based technique to detect it in an efficient way. The brain-imaging data of 98 AD patients and 98 healthy controls was collected using data augmentation method. Then, convolutional neural network (CNN) was used, CNN is the most successful tool in deep learning. An 8-layer CNN was created with optimal structure obtained by experiences. Three activation functions (AFs): sigmoid, rectified linear unit (ReLU), and leaky ReLU. The three pooling-functions were also tested: average pooling, max pooling, and stochastic pooling. The numerical experiments demonstrated that leaky ReLU and max pooling gave the greatest result in terms of performance. It achieved a sensitivity of 97.96%, a specificity of 97.35%, and an accuracy of 97.65%, respectively. In addition, the proposed approach was compared with eight state-of-the-art approaches. The method increased the classification accuracy by approximately 5% compared to state-of-the-art methods.

Original languageEnglish
Article number85
JournalJournal of Medical Systems
Volume42
Issue number5
DOIs
Publication statusPublished - May 2018
Externally publishedYes

Keywords

  • Activation function
  • Alzheimer’s disease
  • Convolutional neural network
  • Data augmentation
  • Leaky rectified linear unit
  • Max pooling

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