Classification of Alzheimer’s Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling
- 337 Downloads
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.
KeywordsAlzheimer’s disease Convolutional neural network Leaky rectified linear unit Max pooling Data augmentation Activation function
This paper was supported by Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Natural Science Foundation of China (61602250) and Natural Science Foundation of Jiangsu Province (BK20150983), and National Institutes of Health (P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584).
Compliance with ethical standards
Conflict of interest
We have no conflicts of interest to disclose with regard to the subject matter of this paper.
- 1.Lange, C. et al., Prediction of Alzheimer's Dementia in Patients with Amnestic Mild Cognitive Impairment in Clinical Routine: Incremental Value of Biomarkers of Neurodegeneration and Brain Amyloidosis Added Stepwise to Cognitive Status. J. Alzheimers Dis. 61(1):373–388, 2018.CrossRefPubMedGoogle Scholar
- 8.Wang, S.-H., Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimed. Tools Appl., 2016. https://doi.org/10.1007/s11042-016-4222-4.
- 9.Sun, J.-D., Multivariate Approach for Alzheimer's disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization. J. Alzheimers Dis., 2017. https://doi.org/10.3233/JAD-170069.
- 25.Gorriz, J. M., and Ramírez, J., Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning. Front. Comput. Neurosci. 10:Article ID. 160, 2016.Google Scholar
- 29.Trakoolwilaiwan, T. et al., Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution. Neurophotonics. 5(1):Article ID. 011008, 2018.Google Scholar
- 30.Chen, Y., Voxelwise detection of cerebral microbleed in CADASIL patients by leaky rectified linear unit and early stopping: A class-imbalanced susceptibility-weighted imaging data study. Multimed Tools Appl (2017). https://doi.org/10.1007/s11042-017-4383-9
- 31.Hara, K., Saito, D., and Shouno, H., Analysis of Function of Rectified Linear Unit Used in Deep learning. in International Joint Conference on Neural Networks. IEEE: Killarney, IRELAND. 144–151, 2015Google Scholar
- 38.Hadgu, A.T., Nigam, A., and Diaz-Aviles, E., Large-Scale Learning with AdaGrad on Spark. in International Conference on Big Data. IEEE: Santa Clara, CA. 2828–2830, 2015Google Scholar