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Sensorineural hearing loss identification via nine-layer convolutional neural network with batch normalization and dropout

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Abstract

Traditional sensorineural hearing loss identification use the framework of feature extraction and classification. Nevertheless, this framework needs manual feature engineering. In this study, we proposed an improved convolutional neural network model to identify hearing loss. Our nine-layer deep convolutional neural network contains six conv layers and three fully-connected layers. We used batch normalization to reduce the impact caused by Internal Covariate shift and dropout techniques to prevent over-fitting to increase the performance in terms of accuracy. Data augmentation was used to enlarge the size of training set. The average results of 10 runs on test set show our method secured sensitivities of left-sided hearing loss, right-sided hearing loss, and healthy controls are 96.33 ± 2.46%, 96.67 ± 2.22%, and 96.67 ± 2.72%, respectively. The overall accuracy of all three classes was 96.56 ± 0.63%. Deep learning can effectively build the identification model. The performance of our proposed nine-layer convolutional neural network model yields better performance than five state-of-the-art approaches.

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Acknowledgements

This paper was supported by Program of Natural Science Research of Jiangsu Higher Education Institutions (16KJB520025, 15KJB470010), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL-1703), Henan Key Research and Development Project (182102310629).

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Authors and Affiliations

  1. Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China

    Shui-Hua Wang

  2. School of Architecture Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, UK

    Shui-Hua Wang

  3. School of Earth Sciences and Engineering, Sun Yat⁃Sen University, Guangzhou, 510275, China

    Jin Hong

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

    Ming Yang

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  1. Shui-Hua Wang
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  2. Jin Hong
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Correspondence to Shui-Hua Wang or Ming Yang.

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Wang, SH., Hong, J. & Yang, M. Sensorineural hearing loss identification via nine-layer convolutional neural network with batch normalization and dropout. Multimed Tools Appl 79, 15135–15150 (2020). https://doi.org/10.1007/s11042-018-6798-3

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