Skip to main content
Springer Nature Link
Log in

An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Note: Condition number is shown to be an effective qualitative measure to find the conditioning of a matrix [44]. It may be noted that an ill-conditioned system has large condition number, while a well-conditioned system has small condition number. The 2-norm condition number of the matrix H can be calculated as,

    $$ \mathcal{K}_{2}(\mathbf{H})=\sqrt{\frac{\lambda_{max}(\mathbf{H}^{T} \mathbf{H})}{\lambda_{min}(\mathbf{H}^{T}\mathbf{H})}} $$
    (8)

    where, λ m a x (H T H) and λ m i n (H T H) denotes the largest and smallest eigenvalues of matrix H T H.

References

  1. Chaplot S., Patnaik L. M., Jagannathan N. R.: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomedical Signal Processing and Control 1(1): 86–92, 2006

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

  3. Das S., Suganthan P. N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1): 4–31, 2011

  4. El-Dahshan E. A., Mohsen H. M., Revett K., Salem A. B. M.: Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst. Appl. 41(11): 5526–5545, 2014

  5. El-Dahshan E. S. A., Honsy T., Salem A. B. M.: Hybrid intelligent techniques for MRI brain images classification. Digital Signal Processing 20(2): 433–441, 2010

  6. Hazlett H. C., Gu H., Munsell B. C., Kim S. H., Styner M., Wolff J. J., Elison J. T., Swanson M. R., Zhu H., Botteron K. N., et al.: Early brain development in infants at high risk for autism spectrum disorder. Nature 542(7641): 348–351, 2017

  7. Huang G. B., Wang D. H., Lan Y.: Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics 2(2): 107–122, 2011

  8. Huang G. B., Zhu Q. Y., Siew C. K.: Extreme learning machine: theory and applications. Neurocomputing 70(1): 489–501, 2006

  9. Islam S. M., Das S., Ghosh S., Roy S., Suganthan P. N.: An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans. Syst. Man Cybern. B (Cybernetics) 42(2): 482–500, 2012

  10. Johnson K. A., Becker J. A. The Whole Brain Atlas. http://www.med.harvard.edu/AANLIB/

  11. Martínez A. M., Kak A. C.: PCA Versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2): 228–233, 2001

  12. Nayak D. R., Dash R., Majhi B.: Classification of brain MR images using discrete wavelet transform and random forests.. In: 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4. IEEE, 2015

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

  14. Nayak D. R., Dash R., Majhi B.: Pathological brain detection using curvelet features and least squares SVM. Multimedia Tools and Applications 75: 1–24, 2016

  15. Nayak D. R., Dash R., Majhi B.: Stationary wavelet transform and adaboost with SVM based pathological brain detection in MRI scanning. CNS & Neurological Disorders Drug Targets 16: 137–149, 2017

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

  17. Pizer S. M., Johnston R. E., Ericksen J. P., Yankaskas B. C., Muller K. E.: Contrast-limited adaptive histogram equalization: speed and effectiveness.. In: Proceedings of the 1st Conference on Visualization in Biomedical Computing, pp. 337–345. IEEE , 1990

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

  19. Storn R., Price K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4): 341–359, 1997

  20. Suresh S., Babu R. V., Kim H.: No-reference image quality assessment using modified extreme learning machine classifier. Appl. Soft Comput. 9(2): 541–552, 2009

  21. Wang S., Li P., Chen P., Phillips P., Liu G., Du S., Zhang Y.: Pathological brain detection via wavelet packet tsallis entropy and real-coded biogeography-based optimization. Fundamenta Informaticae 151(1–4): 275–291, 2017

  22. Wang S., Lu S., Dong Z., Yang J., Yang M., Zhang Y.: Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection. Appl. Sci. 6(6): 169, 2016

  23. Wang S., Zhang Y., Yang X., Sun P., Dong Z., Liu A., Yuan T. F.: Pathological brain detection by a novel image feature—fractional Fourier entropy. Entropy 17(12): 8278–8296, 2015

  24. Xu Y., Shu Y.: Evolutionary extreme learning machine based on particle swarm optimization.. In: International Symposium on Neural Networks, pp. 644–652. Springer, 2006

  25. Xu Y., Zhang D., Yang J., Yang J. Y.: An approach for directly extracting features from matrix data and its application in face recognition. Neurocomputing 71(10): 1857–1865, 2008

  26. Yang G., Zhang Y., Yang J., Ji G., Dong Z., Wang S., Feng C., Wang Q.: Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimedia Tools and Applications 75: 1–17, 2015

  27. Yang J., Yang J. Y.: Why can LDA be performed in PCA transformed space? Pattern Recognit. 36(2): 563–566, 2003

  28. Yang J., Zhang D., Frangi A. F., Yang J. Y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE transactions on pattern analysis and machine intelligence 26(1): 131–137, 2004

  29. Zhang G., Wang Q., Feng C., Lee E., Ji G., Wang S., Zhang Y., Yan J.: Automated classification of brain MR images using wavelet-energy and support vector machines.. In: 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering (MEIC-15), pp. 683–686, 2015

  30. Zhang Y., Dong Z., Liu A., Wang S., Ji G., Zhang Z., Yang J.: Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine. Journal of Medical Imaging and Health Informatics 5(7): 1395–1403, 2015

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

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

  33. Zhang Y., Lu S., Zhou X., Yang M., Wu L., Liu B., Phillips P., Wang S.: Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation 92(9): 861–871, 2016

  34. Zhang Y., Ranjan Nayak D., Yang M., Yuan T. F., Liu B., Lu H., Wang S.: Detection of unilateral hearing loss by stationary wavelet entropy. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 16(2): 122–128, 2017

  35. Zhang Y., Sun Y., Phillips P., Liu G., Zhou X., Wang S.: A multilayer perceptron based smart pathological brain detection system by fractional Fourier entropy. J. Med. Syst. 40(7): 1–11, 2016

  36. Zhang Y., Wang S., Phillips P., Dong Z., Ji G., Yang J.: Detection of alzheimer’s disease and mild cognitive impairment based on structural volumetric MR images using 3d-DWT and WTA-KSVM trained by PSOTVAC. Biomedical Signal Processing and Control 21: 58–73, 2015

  37. Zhang Y., Wang S., Sun P., Phillips P.: Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-medical Materials and Engineering 26(s1): S1283–S1290, 2015

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

  39. Zhang Y., Wu L.: An MR brain images classifier via principal component analysis and kernel support vector machine. Prog. Electromagn. Res. 130: 369–388, 2012

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

  41. Zhang Y. D., Chen S., Wang S. H., Yang J. F., Phillips P.: Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. Int. J. Imaging Syst. Technol. 25(4): 317–327 , 2015

  42. Zhang Y. D., Chen X. Q., Zhan T. M., Jiao Z. Q., Sun Y., Chen Z. M., Yao Y., Fang L. T., Lv Y. D., Wang S. H.: Fractal dimension estimation for developing pathological brain detection system based on Minkowski-Bouligand method. IEEE Access 4: 5937–5947, 2016

  43. Zhang Y. D., Zhang Y., Hou X. X., Chen H., Wang S. H.: Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed. Multimedia Tools and Applications 76: 1–18, 2017

  44. Zhao G., Shen Z., Miao C., Man Z.: On improving the conditioning of extreme learning machine: a linear case.. In: 7Th International Conference on Information, Communications and Signal Processing, ICICS, pp. 1–5. IEEE, 2009

  45. Zhou X., Wang S., Xu W., Ji G., Phillips P., Sun P., Zhang Y.: Detection of pathological brain in MRI scanning based on wavelet-entropy and naive Bayes classifier.. In: Bioinformatics and Biomedical Engineering, pp. 201–209, 2015

  46. Zhu Q. Y., Qin A. K., Suganthan P. N., Huang G. B.: Evolutionary extreme learning machine. Pattern Recognit. 38(10): 1759–1763, 2005

Download references

Author information

Authors and Affiliations

  1. Pattern Recognition Lab, Department of Computer Science and Engineering, National Institute of Technology, Rourkela, 769 008, India

    Deepak Ranjan Nayak, Ratnakar Dash & Banshidhar Majhi

Authors
  1. Deepak Ranjan Nayak
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ratnakar Dash
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Banshidhar Majhi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Deepak Ranjan Nayak.

Ethics declarations

Conflict of interests

We have no conflicts of interest.

Additional information

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

This article is part of the Topical Collection on Advanced Computational Intelligence and Soft Computing in Medical Imaging

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nayak, D.R., Dash, R. & Majhi, B. An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine. J Med Syst 42, 19 (2018). https://doi.org/10.1007/s10916-017-0867-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-017-0867-4

Keywords