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Non-white matter tissue extraction and deep convolutional neural network for Alzheimer’s disease detection

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Abstract

In this paper, we introduce an automatic and robust method to detect and identify Alzheimer’s disease (AD) using the magnetic resonance imaging (MRI) and positron emission tomography (PET) images. AD research as utilized with clinical and computer aid diagnostic tools has been strongly developed in recent decades. Several studies have resulted in many methods of early detection of AD, which benefit patient outcomes and new findings on the development of a deeper understanding of the mechanisms of this disease. Therefore, using the operation of electronic computers to diagnose automatically the incident of AD has served a vital role in supporting clinicians as well as easing significant elaboration on manual and subjectively AD diagnosing of clinicians for the patient’s beneficial outcomes. To this end, we propose a deep learning approach-based model of AD detection applying to MRI and PET images. Individually, we extract non-white matter of brain PET images, which are guided by MRI images as an anatomical mask. Before running the classification module, we build an unsupervised network entitled the high-level layer concatenation autoencoder to pre-train the network with inputs as three-dimensional patches extracted from pre-processed scans. The learned parameters are reused for a well-known convolutional neural network to boost up the training procedure. We conduct experiments on a public data set ADNI and classified a subject into one of three groups: normal control, mild cognitive impairment, and AD. Our proposed method outperforms for AD detection problem than other methods.

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Notes

  1. Available at https://ida.loni.usc.edu. As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this paper.

  2. http://www.fil.ion.ucl.ac.uk/spm/software/spm12/.

  3. https://keras.io/.

  4. https://www.tensorflow.org/.

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Acknowledgements

This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support programme (IITP-2017-2016-0-00314) supervised by the IITP (Institute for Information & communications Technology Promotion) and the Korean government (MSIP) (NRF-2017R1A2B4011409).

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Author notes

  1. Tien-Duong Vu and Ngoc-Huynh Ho have same contribution in this study.

Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea

    Tien-Duong Vu, Ngoc-Huynh Ho & Hyung-Jeong Yang

  2. Department of Nuclear Medicine, Chonnam National University Hospital and Medical School, Gwangju, South Korea

    Jahae Kim & Ho-Chun Song

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  1. Tien-Duong Vu
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  2. Ngoc-Huynh Ho
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Correspondence to Hyung-Jeong Yang.

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Communicated by G. Yi.

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Vu, TD., Ho, NH., Yang, HJ. et al. Non-white matter tissue extraction and deep convolutional neural network for Alzheimer’s disease detection. Soft Comput 22, 6825–6833 (2018). https://doi.org/10.1007/s00500-018-3421-5

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