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2020 | OriginalPaper | Buchkapitel

3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease

verfasst von : Juan Ruiz, Mufti Mahmud, Md Modasshir, M. Shamim Kaiser, for the Alzheimer’s Disease Neuroimaging Initiative

Erschienen in: Brain Informatics

Verlag: Springer International Publishing

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Abstract

One of the major causes of death in developing nations is the Alzheimer’s Disease (AD). For the treatment of this illness, is crucial to early diagnose mild cognitive impairment (MCI) and AD, with the help of feature extraction from magnetic resonance images (MRI). This paper proposes a 4-way classification of 3D MRI images using an ensemble implementation of 3D Densely Connected Convolutional Networks (3D DenseNets) models. The research makes use of dense connections that improve the movement of data within the model, due to having each layer linked with all the subsequent layers in a block. Afterwards, a probability-based fusion method is employed to merge the probabilistic output of each unique individual classifier model. Available through the ADNI dataset, preprocessed 3D MR images from four subject groups (i.e., AD, healthy control, early MCI, and late MCI) were acquired to perform experiments. In the tests, the proposed approach yields better results than other state-of-the-art methods dealing with 3D MR images.

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Vorheriges Kapitel Precise Estimation of Resting State Functional Connectivity Using Empirical Mode Decomposition

Nächstes Kapitel Dynamic Functional Connectivity Captures Individuals’ Unique Brain Signatures

Abrol, A., Bhattarai, M., Fedorov, A., Du, Y., Plis, S., Calhoun, V.: Deep residual learning for neuroimaging: an application to predict progression to Alzheimer’s disease. J. Neurosci. Methods 339, 108701 (2020)CrossRef

Ali, H.M., Kaiser, M.S., Mahmud, M.: Application of convolutional neural network in segmenting brain regions from MRI data. In: Liang, P., Goel, V., Shan, C. (eds.) BI 2019. LNCS, vol. 11976, pp. 136–146. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37078-7_14CrossRef

Association, A.: 2014 Alzheimer’s disease facts and figures. Alzheimer’s Dement. 10(2), e47–e92 (2014)CrossRef

Basaia, S., et al.: Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin. 21, 101645 (2019)CrossRef

Dimitriadis, S.I., Liparas, D., Tsolaki, M.N., Initiative, A.D.N., et al.: Random forest feature selection, fusion and ensemble strategy: combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and Alzheimer’s disease patients: from the Alzheimer’s disease neuroimaging initiative (ADNI) database. J. Neurosci. Methods 302, 14–23 (2018)CrossRef

Dongren, Y., Calhoun, V., Fu, Z., Du, Y., Sui, J.: An ensemble learning system for a 4-way classification of Alzheimer’s disease and mild cognitive impairment. J. Neurosci. Methods 302, 75–81 (2018)CrossRef

Fabietti, M., et al.: Neural network-based artifact detection in local field potentials recorded from chronically implanted neural probes. In: Proceedings of the IJCNN, pp. 1–8 (2020)

Farooq, A., Anwar, S., Awais, M., Rehman, S.: A deep CNN based multi-class classification of Alzheimer’s disease using MRI. In: 2017 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1–6 (2017)

Kaiser, M.S., et al.: Advances in crowd analysis for urban applications through urban event detection. IEEE Trans. Intell. Transp. Syst. 19(10), 3092–3112 (2018)CrossRef

10.

Korolev, S., Safiullin, A., Belyaev, M., Dodonova, Y.: Residual and plain convolutional neural networks for 3D brain MRI classification. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 835–838 (2017)

11.

Liu, S., et al.: ADNI: multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015)CrossRef

12.

Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D.: Early diagnosis of Alzheimer’s disease with deep learning. In: 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, pp. 1015–1018, January 2014

13.

Lu, D., Popuri, K., Ding, G.W., Balachandar, R., Beg, M.F.: Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural mr and FDG-PET images. Sci. Rep. 8(1), 1–13 (2018)CrossRef

14.

Mahmud, M., Kaiser, M.S., Hussain, A.: Deep learning in mining biological data, pp. 1–36. arXiv:2003.00108 [cs, q-bio, stat], February 2020

15.

Mahmud, M., Kaiser, M.S., Rahman, M.M., Rahman, M.A., Shabut, A., Al-Mamun, S., Hussain, A.: A brain-inspired trust management model to assure security in a cloud based IoT framework for neuroscience applications. Cogn. Comput. 10(5), 864–873 (2018)CrossRef

16.

Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2063–2079 (2018)MathSciNetCrossRef

17.

McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., Stadlan, E.: Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA work group under the auspices of department of health and human services task force on Alzheimer’s disease. Neurology 34(7), 939–944 (1984)CrossRef

18.

Miah, Y., Prima, C.N.E., Seema, S.J., Mahmud, M., Kaiser, M.S.: Performance comparison of machine learning techniques in identifying dementia from open access clinical datasets. In: Proceedings of the ICACIn, pp. 69–78. Springer, Singapore (2020)

19.

Noor, M.B.T., Zenia, N.Z., Kaiser, M.S., Mahmud, M., Al Mamun, S.: Detecting neurodegenerative disease from MRI: a brief review on a deep learning perspective. In: Liang, P., Goel, V., Shan, C. (eds.) BI 2019. LNCS, vol. 11976, pp. 115–125. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37078-7_12CrossRef

20.

Orojo, O., Tepper, J., McGinnity, T., Mahmud, M.: A multi-recurrent network for crude oil price prediction. In: Proceedings of the SSCI, pp. 2940–2945, December 2019

21.

Rabby, G., Azad, S., Mahmud, M., Zamli, K.Z., Rahman, M.M.: TeKET: a tree-based unsupervised keyphrase extraction technique. Cogn. Comput. (2020). https://doi.org/10.1007/s12559-019-09706-3

22.

Tang, H., Yao, E., Tan, G., Guo, X.: A fast and accurate 3D fine-tuning convolutional neural network for Alzheimer’s disease diagnosis. In: Zhou, Z.-H., Yang, Q., Gao, Y., Zheng, Yu. (eds.) ICAI 2018. CCIS, vol. 888, pp. 115–126. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-2122-1_9CrossRef

23.

Tania, M.H., et al.: Assay type detection using advanced machine learning algorithms. In: Proceedings of the SKIMA, pp. 1–8 (2019)

24.

Wang, H., et al.: Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing 333, 145–156 (2019)CrossRef

25.

Watkins, J., Fabietti, M., Mahmud, M.: SENSE: a student performance quantifier using sentiment analysis. In: Proceedings of the IJCNN, pp. 1–6 (2020)

26.

Wen, G., Hou, Z., Li, H., Li, D., Jiang, L., Xun, E.: Ensemble of deep neural networks with probability-based fusion for facial expression recognition. Cogn. Comput. 9, 597–610 (2017). https://doi.org/10.1007/s12559-017-9472-6CrossRef

27.

Yahaya, S.W., Lotfi, A., Mahmud, M.: A consensus novelty detection ensemble approach for anomaly detection in activities of daily living. Appl. Soft Comput. 83, 105613 (2019)CrossRef

28.

Yahaya, S.W., Lotfi, A., Mahmud, M., Machado, P., Kubota, N.: Gesture recognition intermediary robot for abnormality detection in human activities. In: Proceedings of the SSCI, pp. 1415–1421, December 2019

29.

Zohora, M.F., et al.: Forecasting the risk of type II diabetes using reinforcement learning. In: Proceedings of the ICIEV, pp. 1–6 (2020)

Titel: 3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease
verfasst von: Juan Ruiz
Mufti Mahmud
Md Modasshir
M. Shamim Kaiser
for the Alzheimer’s Disease Neuroimaging Initiative
Verlag: Springer International Publishing
Buch: Brain Informatics
Print ISBN: 978-3-030-59276-9

Electronic ISBN: 978-3-030-59277-6

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-59277-6_8

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