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

Topic: Brain imaging, connectivity and assessment

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.

@article{wang2018-29577169,
  author  = {Wang, S. H. and Phillips, P. and Sui, Y. and Liu, B. and Yang, M. and Cheng, H.},
  title   = {{Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling}},
  journal = {Journal of medical systems},
  year    = {2018},
  doi     = {10.1007/s10916-018-0932-7},
  pmid    = {29577169},
  url     = {https://doi.org/10.1007/s10916-018-0932-7}
}

Key findings

• The numerical experiments demonstrated that leaky ReLU and max pooling gave the greatest result in terms of performance.
• 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.

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Study details

• Effect size97.96%; 97.35%; 97.65%
• Evidence strengthLow

Read the original

• DOI: 10.1007/s10916-018-0932-7
• PubMed: 29577169
• Original source →

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