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Computer-Aided Dementia Diagnosis Based on Hierarchical Extreme Learning Machine

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Abstract

The deep learning–based computer-aided diagnosis (CADx) approaches of dementia often require a lot of manual intervention. Although deep learning has a good effect on feature extraction, the current deep learning methods usually need to set a large number of parameters manually, which is time consuming. Hierarchical extreme learning machine (H-ELM) needs only less manual intervention and can extract features by a multi-layer feature representation framework, which is much faster than the traditional deep learning methods. A CADx framework based on H-ELM, named DCADx, is proposed. As common spatial pattern (CSP) and brain functional network (BFN) have been proven to have better de-redundancy effects on brain data, the DCADx contains two different data redundancy reduction methods: (1) CSP-based DCADx (i.e., DCADx-CSP model) and (2) BFN-based DCADx (i.e., DCADx-BFN model). The experimental evaluation proved the effectiveness of the proposed algorithms. The DCADx-CSP model obtained 83.2% on Alzheimer’s disease and 82.5% on Parkinson’s disease. The DCADx-BFN obtained 89.3% on Alzheimer’s disease and 88.7% on Parkinson’s disease. DCADx can make full use of the feature expression ability of H-ELM to achieve better performance. CSP and BFN can reduce the redundancy to enhance the diagnostic accuracy further.

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Notes

  1. http://adni.loni.usc.edu/

  2. https://opdc.medsci.ox.ac.uk/opdc-literature

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Funding

This work was financially supported by the National Natural Science Foundation of China (61472069, 61402089, U1401256, 61672146), and the Fundamental Research Funds for the Central Universities (N180101028, N180408019, N161602003, N160601001), and the China Postdoctoral Science Foundation (2019T120216 and 2018M641705), and the fund of Acoustics Science and Technology Laboratory, and the Open Program of Neusoft Institute of Intelligent Healthcare Technology, Co. Ltd (NRIHTOP1802), the Recruitment Program of Global Experts under Grant (01270021814101/022).

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Authors and Affiliations

  1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

    Zhongyang Wang, Zhiqiong Wang & Yue Zhao

  2. School of Computer Science & Engineering, Northeastern University, Shenyang, China

    Junchang Xin

  3. Key Laboratory of Big Data Management and Analytics, Liaoning Province, Northeastern University, Shenyang, China

    Junchang Xin

  4. Neusoft Institute of Intelligent Healthcare Technology. Co. Ltd., Shenyang, China

    Zhiqiong Wang

  5. Acoustics Science and Technology Laboratory, Harbin Engineering University, Harbin, China

    Zhiqiong Wang

  6. Department of Internal Neurology, The Second Hospital of Dalian Medical University, Dalian, China

    Huizi Gu

  7. College of Engineering, The University of Texas at El Paso, Texas, TX, USA

    Wei Qian

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  1. Zhongyang Wang
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Correspondence to Junchang Xin.

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Cite this article

Wang, Z., Xin, J., Wang, Z. et al. Computer-Aided Dementia Diagnosis Based on Hierarchical Extreme Learning Machine. Cogn Comput 13, 34–48 (2021). https://doi.org/10.1007/s12559-019-09708-1

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  • DOI: https://doi.org/10.1007/s12559-019-09708-1

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