Abstract
Dementia has become a social problem in the aging society of advanced countries. Currently, 46.8 million people have dementia worldwide, and that figure is predicted to increase threefold to 130 million people by 2050. Alzheimer’s disease (AD) is the most common form of dementia. The cost of care for AD patients in 2015 was 818 billion US dollars and is expected to increase dramatically in the future, due to the increasing number of patients as a result of the aging society. However, it is still very difficult to cure AD; thus, the detection of AD is crucial. This study proposes the use of machine learning to detect AD using brain image data, with the goal of reducing the cost of diagnosing and caring for AD patients. Most machine learning algorithms rely on good feature representations, which are commonly obtained manually and require domain experts to provide guidance. Feature extraction is a time-consuming and labor-intensive task. In contrast, the 3D Convolutional Neural Network (3DCNN) automatically learns feature representation from images and is not greatly affected by image processing. However, the performance of CNN depends on its layer architecture. This study proposes a novel 3DCNN architecture for MRI image diagnosis of AD.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Krizhevsky, A., et al.: Imagenet classification with deep convolutional neural networks. In: Neural Information Processing Systems, vol. 25, pp. 1097–1105 (2012)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Srivastava, N., et al.: Dropout: a simple way to prevent neural network from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Goodfellow, I.J., et al.: Maxout networks. In: Proceedings on the 30th International Conference on Machine Learning, JMLR: W&CP, vol. 28 (2013)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). CoRR, abs/1409.1556
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. (2014). CoRR, abs/1409.4842
He, K., et al.: Deep residual learning for image recognition (2015). arXiv preprint arXiv:1512.03385
Liu, M., et al.: Inherent structure-based multiview learning with multitemplate feature representation for Alzheimer’s disease diagnosis. IEEE Trans. Biomed. Eng. 63(7), 1473–1482 (2016)
Ji, S., et al.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)
Hosseini-Asl, E., et al.: Alzheimer’s disease diagnostics by adaptation of 3D convolutional network. https://doi.org/10.1109/ICIP.2016.7532332
Phillips, O.R., et al.: Major superficial white matter abnormalities in Huntington disease. Front. Neurosci. 10(0197) (2016). https://doi.org/10.3389/fnins.2016.00197
Alzheimer’s Disease International: The International Federation of Alzheimer’s Disease and Related Disorders Societies
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Karasawa, H., Liu, CL., Ohwada, H. (2018). Deep 3D Convolutional Neural Network Architectures for Alzheimer’s Disease Diagnosis. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10751. Springer, Cham. https://doi.org/10.1007/978-3-319-75417-8_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-75417-8_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75416-1
Online ISBN: 978-3-319-75417-8
eBook Packages: Computer ScienceComputer Science (R0)