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Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images

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Abstract

Automatic binary classification of brain magnetic resonance (MR) images has made remarkable progress in the past decade. In comparison, a few pieces of work has been reported on multiclass classification of brain MR images. However, there exist enough scopes for improved automation and accuracy. Most of the existing schemes follow the multi-stage pipeline structure of conventional machine learning framework, where the features are designed manually or hand-crafted. In recent years, deep learning models have attracted great interest from researchers for analyzing medical images that eliminate the traditional steps of machine learning. In this paper, we present an automated method based on deep extreme learning machine (ELM) also termed as multilayer ELM (ML-ELM) for multiclass classification of the pathological brain. ML-ELM is a multilayer architecture stacked with ELM based autoencoders. The effectiveness of leaky rectified linear unit (LReLU) activation function is investigated with ML-ELM. Extensive simulations on a multiclass brain MR image dataset indicate that the ML-ELM with LReLU activation (ML-ELM+LReLU) achieves higher performance with faster training speed compared to its counterparts as well as state-of-the-art schemes. The basic purpose of employing ML-ELM+LReLU algorithm is to eliminate the need for hand-crafted feature extraction and to develop a more stable and generalized system for multiclass brain MR image classification.

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References

  1. Bartlett PL (1998) The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans Inf Theory 44(2):525–536

    Article  MathSciNet  Google Scholar 

  2. Chaplot S, Patnaik LM, Jagannathan NR (2006) Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 1(1):86–92

    Article  Google Scholar 

  3. Das S, Chowdhury M, Kundu K (2013) Brain MR image classification using multiscale geometric analysis of ripplet. Prog Electromagn Res 137:1–17

    Article  Google Scholar 

  4. El-Dahshan ESA, Honsy T, Salem ABM (2010) Hybrid intelligent techniques for MRI brain images classification. Digital Signal Process 20(2):433–441

    Article  Google Scholar 

  5. Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27–48

    Article  Google Scholar 

  6. Hannun A, Case C, Casper J, Catanzaro B, Diamos G, Elsen E, Prenger R, Satheesh S, Sengupta S, Coates A et al (2014) Deep speech: Scaling up end-to-end speech recognition. arXiv:1412.5567

  7. Hinton GE (2012) A practical guide to training restricted boltzmann machines. In: Neural networks: tricks of the trade. Springer, pp 599–619

  8. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MathSciNet  Google Scholar 

  9. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501

    Article  Google Scholar 

  10. Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122

    Article  Google Scholar 

  11. Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42 (2):513–529

    Article  Google Scholar 

  12. Jia W, Muhammad K, Wang SH, Zhang YD (2017) Five-category classification of pathological brain images based on deep stacked sparse autoencoder. Multimedia Tools and Applications pp 1–20

  13. Johnson KA, Becker JA The Whole Brain Atlas. http://www.med.harvard.edu/AANLIB/

  14. Kalbkhani H, Shayesteh MG, Zali-Vargahan B (2013) Robust algorithm for brain magnetic resonance image (MRI) classification based on garch variances series. Biomed Signal Process Control 8(6):909–919

    Article  Google Scholar 

  15. Kasun LLC, Zhou H, Huang GB, Vong CM (2013) Representational learning with extreme learning machine for big data. IEEE Intell Syst 28(6):31–34

    Google Scholar 

  16. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  17. 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 

  18. Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In: ICML, vol 30, p 3

  19. Nayak DR, Dash R, Majhi B (2016) Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 177:188–197

    Article  Google Scholar 

  20. Nayak DR, Dash R, Majhi B, Prasad V (2017) Automated pathological brain detection system: a fast discrete curvelet transform and probabilistic neural network based approach. Expert Syst Appl 88:152–164

    Article  Google Scholar 

  21. Nayak DR, Dash R, Majhi B (2017) Stationary wavelet transform and adaboost with SVM based pathological brain detection in MRI scanning. CNS Neurol Disord Drug Targets 16(2):137–149

    Article  Google Scholar 

  22. Nayak DR, Dash R, Majhi B (2018) Development of pathological brain detection system using jaya optimized improved extreme learning machine and orthogonal ripplet-ii transform. Multimed Tools Appl 77(17):22,705–22,733

    Article  Google Scholar 

  23. Nayak DR, Dash R, Majhi B (2018) Discrete ripplet-ii transform and modified PSO based improved evolutionary extreme learning machine for pathological brain detection. Neurocomputing 282:232–247

    Article  Google Scholar 

  24. Nayak DR, Dash R, Majhi B (2018) An improved pathological brain detection system based on two-dimensional PCA and evolutionary extreme learning machine. J Med Syst 42(1):19

    Article  Google Scholar 

  25. Nayak DR, Dash R, Majhi B (2018) Pathological brain detection using curvelet features and least squares svm. Multimed Tools Appl 77(3):3833–3856

    Article  Google Scholar 

  26. Salakhutdinov R, Larochelle H (2010) Efficient learning of deep boltzmann machines. In: International conference on artificial intelligence and statistics, pp 693–700

  27. Saritha M, Joseph KP, Mathew AT (2013) Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recogn Lett 34(16):2151–2156

    Article  Google Scholar 

  28. Shi J, Zheng X, Li Y, Zhang Q, Ying S (2018) Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease. IEEE J Biomed Health Inform 22(1):173–183

    Article  Google Scholar 

  29. Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lal S, Usher D (2002) Automated detection of diabetic retinopathy on digital fundus images. Diabet Med 19(2):105–112

    Article  Google Scholar 

  30. Srivastava N, Salakhutdinov RR (2012) Multimodal learning with deep boltzmann machines. In: Advances in neural information processing systems, pp 2222–2230

  31. Tang J, Deng C, Huang GB (2016) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27(4):809–821

    Article  MathSciNet  Google Scholar 

  32. Turner JA, Potkin SG, Brown GG, Keator DB, McCarthy G, Glover GH (2007) Neuroimaging for the diagnosis and study of psychiatric disorders. IEEE Signal Proc Mag 24(4):112–117

    Article  Google Scholar 

  33. Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: International conference on machine learning. ACM, pp 1096–1103

  34. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(Dec):3371–3408

    MathSciNet  MATH  Google Scholar 

  35. Wang S, Phillips P, Yang J, Sun P, Zhang Y (2016) Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients. Biomedical Engineering/Biomedizinische Technik, pp 1–10

  36. Wang S, Du S, Atangana A, Liu A, Lu Z (2018) Application of stationary wavelet entropy in pathological brain detection. Multimed Tools Appl 77(3):3701–3714

    Article  Google Scholar 

  37. Wang S, Zhang Y, Zhan T, Phillips P, Zhang Y, Liu G, Lu S, Wu X (2016) Pathological brain detection by artificial intelligence in magnetic resonance imaging scanning. Prog Electromagn Res 156:105–133

    Article  Google Scholar 

  38. Wong TY, Bressler NM (2016) Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. Jama 316(22):2366–2367

    Article  Google Scholar 

  39. Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. arXiv:1505.00853

  40. Yang G, Zhang Y, Yang J, Ji G, Dong Z, Wang S, Feng C, Wang Q (2016) Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimed Tools Appl 75(23):15,601–15,617

    Article  Google Scholar 

  41. Zhang Y, Wang S, Wu L (2010) A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO. Prog Electromagn Res 109:325–343

    Article  Google Scholar 

  42. Zhang Y, Dong Z, Wu L, Wang S (2011) A hybrid method for MRI brain image classification. Expert Syst Appl 38(8):10,049–10,053

    Article  Google Scholar 

  43. Zhang Y, Wu L, Wang S (2011) Magnetic resonance brain image classification by an improved artificial bee colony algorithm. Prog Electromagn Res 116:65–79

    Article  Google Scholar 

  44. Zhang Y, Wang S, Ji G, Dong Z (2013) An MR brain images classifier system via particle swarm optimization and kernel support vector machine. Sci World J 2013:1–9

    Google Scholar 

  45. Zhang Y, Dong Z, Ji G, Wang S (2015) Effect of spider-web-plot in MR brain image classification. Pattern Recogn Lett 62:14–16

    Article  Google Scholar 

  46. Zhang Y, Dong Z, Wang S, Ji G, Yang J (2015) Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine. Entropy 17(4):1795–1813

    Article  Google Scholar 

  47. Zhang YD, Zhao G, Sun J, Wu X, Wang ZH, Liu HM, Govindaraj VV, Zhan T, Li J (2017) Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and jaya algorithm. Multimedia Tools and Applications, pp 1–20

  48. Zhang YD, Jiang Y, Zhu W, Lu S, Zhao G (2018) Exploring a smart pathological brain detection method on pseudo zernike moment. Multimed Tools Appl 77(17):22,589–22,604

    Article  Google Scholar 

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Correspondence to Deepak Ranjan Nayak.

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Nayak, D.R., Das, D., Dash, R. et al. Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images. Multimed Tools Appl 79, 15381–15396 (2020). https://doi.org/10.1007/s11042-019-7233-0

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  • DOI: https://doi.org/10.1007/s11042-019-7233-0

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