Skip to main content
Top

2019 | OriginalPaper | Chapter

Discovering Senile Dementia from Brain MRI Using Ra-DenseNet

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

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

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!

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!

Literature
1.
go back to reference 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) 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)
2.
go back to reference 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 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
3.
go back to reference Harman, D.: Alzheimer’s disease pathogenesis. Ann. N. Y. Acad. Sci. 1067, 454–560 (2007)CrossRef Harman, D.: Alzheimer’s disease pathogenesis. Ann. N. Y. Acad. Sci. 1067, 454–560 (2007)CrossRef
4.
go back to reference 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) 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)
5.
go back to reference 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) 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)
6.
go back to reference 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) 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)
7.
go back to reference Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014) Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)
8.
go back to reference 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) 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)
9.
go back to reference 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) 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.
go back to reference LeCun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef LeCun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef
11.
go back to reference Liu, M., Zhang, D., Shen, D.: Ensemble sparse classification of Alzheimer’s disease. Neuroimage 60(2), 1106–1116 (2012)CrossRef Liu, M., Zhang, D., Shen, D.: Ensemble sparse classification of Alzheimer’s disease. Neuroimage 60(2), 1106–1116 (2012)CrossRef
12.
go back to reference Liu, Y.: Magnetic resonance imaging. In: Current Laboratory Methods in Neuroscience Research, pp. 249–270 (2013) Liu, Y.: Magnetic resonance imaging. In: Current Laboratory Methods in Neuroscience Research, pp. 249–270 (2013)
13.
go back to reference 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 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.
go back to reference 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) 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.
go back to reference 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) 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.
go back to reference 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 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.
go back to reference 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 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.
go back to reference 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 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.
go back to reference 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 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.
go back to reference 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) 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.
go back to reference 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) 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.
go back to reference Russakovsky, O., Deng, J., Su, H., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRef 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.
go back to reference 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 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.
go back to reference 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 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.
go back to reference 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) 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.
go back to reference 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 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.
go back to reference 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) 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.
go back to reference 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 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.
go back to reference 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) 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.
go back to reference 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 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.
go back to reference 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 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.
go back to reference 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 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.
go back to reference 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) 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.
go back to reference 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 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
Metadata
Title
Discovering Senile Dementia from Brain MRI Using Ra-DenseNet
Authors
Xiaobo Zhang
Yan Yang
Tianrui Li
Hao Wang
Ziqing He
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-16142-2_35

Premium Partner