Abstract
Deep learning has been a successful model which can effectively represent several features of input space and remarkably improve image recognition performance on the deep architectures. In our research, an adaptive structural learning method of restricted Boltzmann machine (adaptive RBM) and deep belief network (adaptive DBN) has been developed as a deep learning model. The models have a self-organize function which can discover an optimal number of hidden neurons for given input data in a RBM by neuron generation–annihilation algorithm and can obtain an appropriate number of RBMs as hidden layers. In this paper, the proposed model was applied to MRI and PET image datasets in ADNI digital archive for the early detection of mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Two kinds of deep learning models were constructed to classify the MRI and PET images. For the training set, our model showed 99.6 and 99.4% classification accuracy for MRI and PET images. For the test set, the model showed 87.6 and 98.5% accuracy for them. Our model achieved the highest classification accuracy among the other CNN models.
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References
Markets and Markets: http://www.marketsandmarkets.com/Market-Reports/deep-learning-market-107369271.html. Accessed 28 Nov 2018 (2016)
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. Arch. 2(1), 1–127 (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems 25 (NIPS 2012) (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedinga of International Conference on Learning Representations (ICLR 2015) (2015)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of CVPR2015 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Russakovsky, O., Deng, J., Su, H., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Kamada, S., Ichimura, T., Hara, A., Mackin, K.J.: Adaptive structure learning method of deep belief network using neuron generation-annihilation and layer generation. Neural Comput. Appl. 1–15 (2018). https://doi.org/10.1007/s00521-018-3622-y
Kamada, S., Ichimura, T.: An adaptive learning method of restricted Boltzmann machine by neuron generation and annihilation algorithm. In: Proceedings of 2016 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2016), pp. 1273–1278 (2016)
Kamada, S., Ichimura, T.: A structural learning method of restricted Boltzmann machine by neuron generation and annihilation algorithm. In: Neural Information Processing, Lecture Notes in Computer Science (LNCS), vol. 9950, pp. 372–380 (2016)
Hinton, G.E.: A practical guide to training restricted Boltzmann machines. In: Neural Networks, Tricks of the Trade, Lecture Notes in Computer Science (LNCS), vol. 7700, pp. 599–619 (2012)
Kamada, S., Ichimura, T.: An adaptive learning method of deep belief network by layer generation algorithm. In: Proceedings of 2016 IEEE Region 10 Conference (TENCON), pp. 2971–2974 (2016)
Hinton, G.E., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Krizhevsky, A.: Learning multiple layers of features from Tiny images. Master of thesis, University of Toronto (2009)
Alzheimer’s Disease Neuroimaging Initiative: http://adni.loni.usc.edu/ (2021)
Liu, M., Cheng, D., Yan, W., et.al.: Classification of Alzheimer’s disease by combination of convolutional and recurrent neural networks using FDG-PET images. Front. Neuroinform. 12(35) (2018)
Liu, M., Cheng, D., Wang, K., et al.: Multi-modality cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Neuroinformatics 16, 295–308 (2018)
Kavitha, M., Yudistira, N., Kurita, T.: Multi instance learning via deep CNN for multi-class recognition of Alzheimer’s disease. In: Proceedings of 2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA), pp. 89–94 (2019)
Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)
Ichimura, T., Tazaki, E., Yoshida, K.: Extraction of fuzzy rules using neural networks with structure level adaptation-verification to the diagnosis of hepatobiliary disorders. Int. J. Biomed. Comput. 40(2), 139–146 (1995)
Acknowledgements
This work was supported by JSPS KAKENHI Grant Number 19K12142, 19K24365 and obtained from the commissioned research by National Institute of Information and Communications Technology (NICT, 21405), Japan.
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Kamada, S., Ichimura, T., Harada, T. (2021). Image-Based Early Detection of Alzheimer’s Disease by Using Adaptive Structural Deep Learning. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2765-1_49
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