nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Discovering Senile Dementia from Brain MRI Using Ra-DenseNet

verfasst von : Xiaobo Zhang, Yan Yang, Tianrui Li, Hao Wang, Ziqing He

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

With the rapid development of medical industry, there is a growing demand for disease diagnosis using machine learning technology. The recent success of deep learning brings it to a new height. This paper focuses on application of deep learning to discover senile dementia from brain magnetic resonance imaging (MRI) data. In this work, we propose a novel deep learning model based on Dense convolutional Network (DenseNet), denoted as ResNeXt Adam DenseNet (Ra-DenseNet), where each block of DenseNet is modified using ResNeXt and the adapter of DenseNet is optimized by Adam algorithm. It compresses the number of the layers in DenseNet from 121 to 40 by exploiting the key characters of ResNeXt, which reduces running complexity and inherits the advantages of Group Convolution technology. Experimental results on a real-world MRI data set show that our Ra-DenseNet achieves a classification accuracy with 97.1\(\%\) and outperforms the existing state-of-the-art baselines (i.e., LeNet, AlexNet, VGGNet, ResNet and DenseNet) dramatically.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Multi-Constraints-Based Enhanced Class-Specific Dictionary Learning for Image Classification

Nächstes Kapitel Granger Causality for Heterogeneous Processes

Fang, C., Li, C., Cabrerizo, M., et al.: A Gaussian discriminant analysis-based generative learning algorithm for the early diagnosis of mild cognitive impairment in Alzheimer’s disease. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, pp. 538–542 (2017)

Gray, K.R., Aljabar, P., Heckemann, R.A., et al.: Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease. NeuroImage 65, 167–175 (2013)CrossRef

Harman, D.: Alzheimer’s disease pathogenesis. Ann. N. Y. Acad. Sci. 1067, 454–560 (2007)CrossRef

He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

Huang, G., Liu, Z., Weinberger, K.Q., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 4700–4708 (2017)

Hon, M., Khan, N.M: Towards Alzheimer’s disease classification through transfer learning. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, pp. 1166–1169 (2017)

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)

Kong, W., Mou, X., Hu, X.: Exploring matrix factorization techniques for significant genes identification of Alzheimers disease microarray gene expression data. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, vol. 12, no. 5, p. S7 (2011)

Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, vol. 1, pp. 1097–1105 (2012)

10.

LeCun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef

11.

Liu, M., Zhang, D., Shen, D.: Ensemble sparse classification of Alzheimer’s disease. Neuroimage 60(2), 1106–1116 (2012)CrossRef

12.

Liu, Y.: Magnetic resonance imaging. In: Current Laboratory Methods in Neuroscience Research, pp. 249–270 (2013)

13.

Liu, F., Wee, C.Y., Chen, H., et al.: Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s disease and mild cognitive impairment identification. NeuroImage 84, 466–475 (2014)CrossRef

14.

Liu, Q., Chen, C., Gao, A., et al.: VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in genome-wide association studies: applied to Alzheimer’s disease. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, pp. 2177–2182 (2017)

15.

Luo, Y.M., Weng, H., Zhang, L., et al.: Salt restriction: recognition and treatment of chronic kidney disease related edema in ancient literature mining. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, pp. 1369–1375 (2017)

16.

Marcus, D., Wang, T., Parker, J., et al.: Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adult. J. Cogn. Neurosci. 19(9), 1498–1507 (2007)CrossRef

17.

Milletari, F., Ahmadi, S.-A., Kroll, C., et al.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput. Vis. Image Underst. 164, 92–102 (2017)CrossRef

18.

Moradi, E., Pepe, A., Gaser, C., et al.: Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage 104, 398–412 (2015)CrossRef

19.

Nichols, T.E., Das, S., Eickhoff, S.B., et al.: Best practices in data analysis and sharing in neuroimaging using MRI. Nat. Neurosci. 20(3), 299–303 (2017)CrossRef

20.

Panda, A.K., Kumar, M., Chaudhary, M.K., et al.: Brain tumour extraction from MRI images using k-means clustering. Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng. 4(4), 356–359 (2016)

21.

Peng, Y., Tang, C., Chen, G., et al.: Multi-label learning by exploiting label correlations for TCM diagnosing Parkinson’s disease. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, pp. 590–594 (2017)

22.

Russakovsky, O., Deng, J., Su, H., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRef

23.

Srivastava, N., Hinton, G., Krizhevsky, A., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetMATH

24.

Sorg, C., Riedl, V., Muhlau, M., et al.: Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proc. Natl. Acad. Sci. 104(47), 18760–18765 (2007)CrossRef

25.

Sutskever, I., Martens, J., Dahl, G., et al.: On the importance of initialization and momentum in deep learning. In: Proceedings of the International Conference on Machine Learning, pp. 1139–1147 (2013)

26.

Tahmasian, M., Shao, J., Meng, C., et al.: Based on the network degeneration hypothesis: separating individual patients with different neurodegenerative syndromes in a preliminary hybrid PET/MR study. J. Nucl. Med. 57, 410–415 (2016)CrossRef

27.

Tang, X., Hu, X., Yang, X., et al.: A algorithm for identifying disease genes by incorporating the subcellular localization information into the protein-protein interaction networks. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, pp. 308–311 (2016)

28.

Tong, T., Gray, K., Gao, Q., et al.: Multi-modal classification of Alzheimer’s disease using nonlinear graph fusion. Pattern Recogn. 63, 171–181 (2017)CrossRef

29.

Xie, S., Girshick, R., Doll\(\acute{a}\)r, P., et al.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5987–5995 (2017)

30.

Young, J., Modat, M., Cardoso, M.J., et al.: Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment. NeuroImage: Clin. 2, 735–745 (2013)CrossRef

31.

Zhang, D., Wang, Y., Zhou, L., et al.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55(3), 856–867 (2011)CrossRef

32.

Zhang, D., Shen, D.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 59(2), 60–67 (2012)MathSciNet

33.

Zhang, X., Yang, Y., Wang, H., et al.: Analysis of senile dementia from the brain magnetic resonance imaging data with clustering. In: Proceedings of the 13th International FLINS Conference (FLINS 2018) and Intelligent Systems and Knowledge Engineering (ISKE 2018), pp. 1454–1461 (2018)

34.

Zhu, X., Suk, H.I., Wang, L., et al.: A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med. Image Anal. 38, 205–214 (2017)CrossRef

Titel: Discovering Senile Dementia from Brain MRI Using Ra-DenseNet
verfasst von: Xiaobo Zhang
Yan Yang
Tianrui Li
Hao Wang
Ziqing He
Verlag: Springer International Publishing
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-3-030-16141-5

Electronic ISBN: 978-3-030-16142-2

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-16142-2_35

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"