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An Improved Deep Polynomial Network Algorithm for Transcranial Sonography–Based Diagnosis of Parkinson’s Disease

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Abstract

Transcranial sonography (TCS) is a valid neuroimaging tool for the diagnosis of Parkinson’s disease (PD). The TCS-based computer-aided diagnosis (CAD) has attracted increasing attention in recent years, in which feature representation and pattern classification are two critical issues. Deep polynomial network (DPN) is a newly proposed deep learning algorithm that has shown its advantage in learning effective feature representation for samples with a small size. In this work, an improved DPN algorithm with enhanced performance on both feature representation and classification is proposed. First, the empirical kernel mapping (EKM) algorithm is embedded into DPN (EKM-DPN) to improve its feature representation. Second, the network pruning strategy is utilized in the EKM-DPN (named P-EKM-DPN). It not only produces robust feature representation, but also addresses the overfitting issues for the subsequent classifiers to some extent. Lastly, the generalization ability is further enhanced by applying the Dropout approach to P-EKM-DPN (D-P-EKM-DPN). The proposed D-P-EKM-DPN algorithm has been evaluated on a TCS dataset with 153 samples. The experimental results indicate that D-P-EKM-DPN outperforms all the compared algorithms and achieves the best classification accuracy, sensitivity, and specificity of 86.95 ± 3.15%, 85.77 ± 7.87%, and 87.16 ± 6.50%, respectively. The proposed D-P-EKN-DPN algorithm has a great potential in TCS-based CAD for PD due to its excellent performance.

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Funding

This work is supported by the National Natural Science Foundation of China (61471231, 81830058, 81627804), the Shanghai Science and Technology Foundation (17411953400, 18010500600, 18411967400), the Jiangsu Commission of Health (Y2018105),  and the Pre-Research Project of The Second Affiliated Hospital of Soochow University (SDFEYGJ1709).

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Authors and Affiliations

  1. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, No. 99, Shangda Road, Shanghai, 200444, China

    Lu Shen, Jun Shi, Lu Chen & Qi Zhang

  2. Department of Ultrasonography, Shanghai East Hospital of Tongji University, Shanghai, China

    Yun Dong

  3. Department of Mathematics, School of Science, Shanghai University, Shanghai, China

    Shihui Ying & Yaxin Peng

  4. Department of Neurology, Shanghai East Hospital of Tongji University, Shanghai, China

    Hedi An

  5. Department of Ultrasound, The Second Affiliated Hospital of Soochow University, Suzhou, China

    Yingchun Zhang

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  1. Lu Shen
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  4. Shihui Ying
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Correspondence to Jun Shi or Yingchun Zhang.

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Shen, L., Shi, J., Dong, Y. et al. An Improved Deep Polynomial Network Algorithm for Transcranial Sonography–Based Diagnosis of Parkinson’s Disease. Cogn Comput 12, 553–562 (2020). https://doi.org/10.1007/s12559-019-09691-7

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