Skip to main content
SpringerLink
Log in

Sensorineural hearing loss classification via deep-HLNet and few-shot learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

We propose a new method for hearing loss classification from magnetic resonance image (MRI), which can automatically detect tissue-specific features in a given MRI. Sensorineural hearing loss (SHNL) is highly prevalent in our society. Early diagnosis and intervention have a profound impact on patient outcomes. A solution to provide early diagnosis is the use of automated diagnostic systems. In this study, we propose a novel Deep-HLNet framework, based on few-shot learning, for the automated classification of SNHL. This research involves magnetic resonance (MRI) images from 60 participants of three balanced categories: left-sided SNHL, right-sided SNHL, and healthy controls. A convolutional neural network was employed for feature extraction from individual categories, while a neural network and a comparison classifier strategy constituted a tri-classifier for SNHL classification. In terms of experiment results and practicability of the algorithm, the classification performance was significantly better than the standard deep learning methods or other conventional methods, with an overall accuracy of 96.62%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Altaf F, Islam SMS, Akhtar N, Janjua NK (2019) Going deep in medical image analysis: Concepts, methods, challenges, and future directions. IEEE Access 7:99540–99572

    Article  Google Scholar 

  2. Antoniou A, Storkey A, Edwards H (2017) Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340

  3. Bahadure NB, Ray AK, Thethi HP (2017) Image analysis for mri based brain tumor detection and feature extraction using biologically inspired bwt and svm. Int J Biomed Imaging 2017:12. https://doi.org/10.1155/2017/9749108

  4. Breininger K, Albarqouni S, Kurzendorfer T, Pfister M, Kowarschik M, Maier A (2018) Intraoperative stent segmentation in x-ray fluoroscopy for endovascular aortic repair. Int J Comput Assist Radiol Surg 13(8):1221–1231

    Article  Google Scholar 

  5. Cai W, Wei Z (2020) Piigan: generative adversarial networks for pluralistic image inpainting arXiv: Computer Vision and Pattern Recognition

  6. Chen Y, Yang M, Chen X, Liu B, Wang H, Wang S (2018) 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(3):3775–3793

    Article  Google Scholar 

  7. Chen Z, Yanwei F, Zhang Y, Jiang Y-G, Xue X, Sigal L (2019) Multi-level semantic feature augmentation for one-shot learning. IEEE Trans Image Process 28(9):4594–4605

    Article  MathSciNet  Google Scholar 

  8. Eklund A, Dufort P, Forsberg D, LaConte SM (2013) Medical image processing on the gpu–past, present and future. Med Image Anal 17(8):1073–1094

    Article  Google Scholar 

  9. Fan G, Peng L, Hong W, Sun F (2016) Electric load forecasting by the svr model with differential empirical mode decomposition and auto regression. Neurocomputing 173:958–970

    Article  Google Scholar 

  10. Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning (PMLR) 70:1126–1135

  11. He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, pp 770–778

  12. Hong W (2011) Electric load forecasting by seasonal recurrent svr (support vector regression) with chaotic artificial bee colony algorithm. Energy 36(9):5568–5578

    Article  Google Scholar 

  13. Hong W, Dong Y, Zhang WY, Chen L, Panigrahi BK (2013) Cyclic electric load forecasting by seasonal svr with chaotic genetic algorithm. Int J Electr Power Energy Syst 44(1):604–614

    Article  Google Scholar 

  14. Hosseini-Asl E, Gimel’farb G, and El-Baz A (2016) Alzheimer’s disease diagnostics by a deeply supervised adaptable 3d convolutional network. arXiv preprint arXiv:1607.00556

  15. Kim E, Corte-Real M, Baloch Z (2016) A deep semantic mobile application for thyroid cytopathology. In Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, vol 9789. International Society for Optics and Photonics, pp 97890A

  16. Kim J, Kim T, Kim S, Yoo CD (2019) Edge-labeling graph neural network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 11–20

    Google Scholar 

  17. Lee K, Maji S, Ravichandran A, Soatto S (2019) Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 10657–10665

    Google Scholar 

  18. Li M, Geng J, Hong W, Zhang L (2019) Periodogram estimation based on lssvr-ccpso compensation for forecasting ship motion. Nonlinear Dyn 97(4):2579–2594

    Article  Google Scholar 

  19. Pereira A (2017). Hu Moment invariant: a new method for hearing loss detection. In: Proceedings of the 2017 International Conference Advanced Engineering and Technology Research (AETR 2017), Xi'an, China. Atlantis Press, Amsterdam, pp 421–416

  20. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  Google Scholar 

  21. Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D (2014) Early diagnosis of alzheimer’s disease with deep learning. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI), Beijing, China. pp 1015–1018. https://doi.org/10.1109/ISBI.2014.6868045

  22. Medela A, Picon A, Saratxaga CL, Belar O, Cabezón V, Cicchi R, Bilbao R, Glover B Few shot learning in histopathological images: reducing the need of labeled data on biological datasets. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, pp 2019, 1860–1864

  23. Mondal AK, Dolz J, Desrosiers C (2018) Few-shot 3d multi-modal medical image segmentation using generative adversarial learning. arXiv preprint arXiv:1810.12241

  24. Muus JS, Weir FW, Kreicher KL, Bowlby DA, Discolo CM, Meyer TA (2017) Hearing loss in children with growth hormone deficiency. Int J Pediatr Otorhinolaryngol 100:107–113

    Article  Google Scholar 

  25. Nayeem A (2017) Hearing loss detection based on wavelet entropy and genetic algorithm. Adv Intell Syst Res 153:49–53

    Google Scholar 

  26. Payan A, Montana G (2015) Predicting alzheimer’s disease: a neuroimaging study with 3d convolutional neural networks. arXiv preprint arXiv:1502.02506

  27. Ravi S, Larochelle H (2016) Optimization as a model for few-shot learning. In: International Conference on Learning Representations (ICLR), Toulon, France

  28. Razzak MI, Naz S, Zaib A (2018) Deep learning for medical image processing: Overview, challenges and the future. In: Dey N, Ashour A, Borra S (eds) Classification in BioApps. Lecture Notes in Computational Vision and Biomechanics, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-65981-7_12

  29. Ren M, Triantafillou E, Ravi S, Snell J, Swersky K, Tenenbaum JB, Larochelle H, Zemel RS (2018) Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676

  30. RPK P, Lamata P, Montana G (2016) Recurrent fully convolutional neural networks for multi-slice mri cardiac segmentation. In: Reconstruction, segmentation, and analysis of medical images. Springer, pp 83–94

  31. Santoro A, Bartunov S, Botvinick M, Wierstra D, and Lillicrap T (2016) One-shot learning with memory-augmented neural networks. arXiv preprint arXiv:1605.06065

  32. Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):60

    Article  Google Scholar 

  33. Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. In: Advances in neural information processing systems 30(NIPS), Long Beach, CA, pp 4077–4087

  34. Sung F, Yang Y, Zhang L, Xiang T, Torr PHS, Hospedales TM (2018) Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, pp 1199–1208

  35. Tang Y (2013) Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239

  36. Vapnik V (2013) The nature of statistical learning theory. Springer science & business media. New York. https://doi.org/10.1007/978-1-4757-3264-1

  37. Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. In: Advances in neural information processing systems, pp 3630–3638

    Google Scholar 

  38. Wang S, Zhang Y, Yang M, Liu B, Ramirez J, Gorriz JM (2017) Preliminary study on unilateral sensorineural hearing loss identification via dual-tree complex wavelet transform and multinomial logistic regression. In: Ferrández VJ, Álvarez-Sánchez J, de la Paz López F, Toledo Moreo J, Adeli H (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science, vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_28

  39. Wang S (2017) Hearing loss detection in medical multimedia data by discrete wavelet packet entropy and single-hidden layer neural network trained by adaptive learning-rate back propagation. In: Cong F, Leung A, Wei Q (eds) Advances in Neural Networks ISNN 2017. Lecture Notes in Computer Science, vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_63

  40. Wang S, Yang M, Li J, Wu X, Wang H, Liu B, Dong Z, Zhang Y (2017) Texture analysis method based on fractional fourier entropy and fitness-scaling adaptive genetic algorithm for detecting left-sided and right-sided sensorineural hearing loss. Fundam Inf 151(1–4):505–521

    Article  MathSciNet  Google Scholar 

  41. Wang S-H, Hong J, Yang M (2018) Sensorineural hearing loss identification via nine-layer convolutional neural network with batch normalization and dropout. Multimed Tools Appl:1–16

  42. Wang Y, Girshick R, Hebert M, Hariharan B (2018) Low-shot learning from imaginary data. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) Salt Lake City, pp 7278–7286

  43. Wang Y, Yao Q, Kwok J, Ni LM (2019) Generalizing from a few examples: A survey on few-shot learning. arXiv preprint arXiv:1904.05046

  44. Wang S, Yang M, Liu S, Zhang Y (2020) Sensorineural hearing loss identification via discrete wavelet packet entropy and cat swarm optimization. In: Dey N, Ashour A, Bhattacharyya S (eds) Applied Nature-Inspired Computing: Algorithms and Case Studies. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-9263-4_6

  45. You H, Tian S, Yu L, Lv Y (2020) Pixel-level remote sensing image recognition based on bidirectional word vectors. IEEE Trans Geosci Remote Sens 58(2):1281–1293

    Article  Google Scholar 

  46. Zhang Z, Hong W (2019) Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm. Nonlinear Dyn 98(4):1107–1136. https://doi.org/10.1007/s11071-019-05252-7

  47. Zhang Z, Hong W, Li J (2020) Electric load forecasting by hybrid self-recurrent support vector regression model with variational mode decomposition and improved cuckoo search algorithm. IEEE Access 8:14642–14658

    Article  Google Scholar 

  48. Zhao A, Balakrishnan G, Durand F, Guttag JV, Dalca AV (2019) Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Long Beach, pp 8543–8553

Download references

Acknowledgements

This work was supported in part by the Royal Society International Exchanges Cost Share Award, UK(RP202G0230), in part by the Medical Research Council Confidence in Concept Award, UK (MC_PC_17171), in part by the Hope Foundation for Cancer Research, UK(RM60G0680) and in part by the British Heart Foundation Accelerator Award, UK. This work was supported by the study abroad program for graduate student of Guilin University of Electronic Technology.

Author information

Authors and Affiliations

  1. Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, 541004, China

    Xi Chen, Rushi Lan & Xiaonan Luo

  2. Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Xi Chen, Qinghua Zhou, Shui-Hua Wang & Yu-Dong Zhang

Authors
  1. Xi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qinghua Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rushi Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shui-Hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu-Dong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaonan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Rushi Lan or Yu-Dong Zhang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, X., Zhou, Q., Lan, R. et al. Sensorineural hearing loss classification via deep-HLNet and few-shot learning. Multimed Tools Appl 80, 2109–2122 (2021). https://doi.org/10.1007/s11042-020-09702-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09702-y

Keywords

Search

Navigation