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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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_14
Association, A.: 2014 Alzheimer’s disease facts and figures. Alzheimer’s Dement. 10(2), e47–e92 (2014)
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)
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)
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)
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)
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)
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)
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
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)
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
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)
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)
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)
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)
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_12
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
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
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_9
Tania, M.H., et al.: Assay type detection using advanced machine learning algorithms. In: Proceedings of the SKIMA, pp. 1–8 (2019)
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)
Watkins, J., Fabietti, M., Mahmud, M.: SENSE: a student performance quantifier using sentiment analysis. In: Proceedings of the IJCNN, pp. 1–6 (2020)
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/s12559-017-9472-6
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)
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
Zohora, M.F., et al.: Forecasting the risk of type II diabetes using reinforcement learning. In: Proceedings of the ICIEV, pp. 1–6 (2020)
Acknowledgment
*Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). For up-to-date information, see http://adni-info.org/.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ruiz, J., Mahmud, M., Modasshir, M., Shamim Kaiser, M., Alzheimer’s Disease Neuroimaging Initiative, f.t. (2020). 3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-59277-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59276-9
Online ISBN: 978-3-030-59277-6
eBook Packages: Computer ScienceComputer Science (R0)