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Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and Tikhonov regularization

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Abstract

In the past, scholars used various computer vision and artificial intelligence methods to detect brain diseases via magnetic resonance imaging (MRI). In this paper, we proposed a novel system to detect sensorineural hearing loss (SNHL). First, we used three-level bior4.4 wavelet to decompose original brain image. Second, principal component analysis (PCA) was utilized for dimensionality reduction. Third, the generalized eigenvalue proximal support vector machine (GEPSVM) with Tikhonov regularization was employed as the classifier. The 10 repetitions of five-fold cross validation showed our method achieved an overall accuracy of 95.71 %. Our sensitivities over healthy control, left-sided SNHL, and right-sided SNHL are 96.00 %, 95.33 %, and 95.71 %, respectively. The proposed system is promising and effective in SNHL detection. It gives better performance than four state-of-the-art methods.

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Acknowledgment

This paper was supported by NSFC (61602250, 61503188, 61562041), Natural Science Foundation of Jiangsu Province (BK20150983, BK20150982), Program of Natural Science Research of Jiangsu Higher Education Institutions (14KJB520021), Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology (2016WLZC013), Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing (BD201607), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology (30916014107).

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, 210023, China

    Yi Chen, Hainan Wang & Shuihua Wang

  2. Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Policy Academy, Changsha, Hunan, 410138, China

    Yi Chen

  3. Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing, 210094, China

    Yi Chen & Hainan Wang

  4. Department of Radiology, Nanjing Children’s Hospital, Nanjing Medical University, Nanjing, 210008, China

    Ming Yang

  5. Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University, Quanzhou Normal University, Quanzhou, Fujian, 362002, China

    Ming Yang & Hainan Wang

  6. Department of electrical engineering, College of engineering, Zhejiang Normal University, Jinhua, Zhejiang, 321004, China

    Xianqing Chen

  7. Department of Radiology, Zhong-Da Hospital of Southeast University, Nanjing, 210009, China

    Bin Liu

  8. Department of Electrical Engineering, The city college of New York, CUNY, New York, NY, 10031, USA

    Shuihua Wang

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  1. Yi Chen
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  2. Ming Yang
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Correspondence to Shuihua Wang.

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Yi Chen, Ming Yang and Xianqing Chen contribute equally to this paper

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Chen, Y., Yang, M., Chen, X. et al. Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and Tikhonov regularization. Multimed Tools Appl 77, 3775–3793 (2018). https://doi.org/10.1007/s11042-016-4087-6

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  • DOI: https://doi.org/10.1007/s11042-016-4087-6

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