Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3715–3728 | Cite as

A pathological brain detection system based on kernel based ELM

  • Siyuan Lu
  • Zhihai Lu
  • Jianfei Yang
  • Ming Yang
  • Shuihua Wang


Magnetic resonance (MR) imaging is widely used in daily medical treatment. It could help in pre-surgical, diagnosis, prognosis, and postsurgical processes. It could be beneficial for diagnosis to classify MR images of brain into healthy or abnormal automatically and accurately, since the information set MRIs generate is too large to interpret with manual methods. We propose a new approach with wavelet-entropy as the features and the kernel based extreme learning machine (K-ELM) to be the classifier. Our method employs 2D-discreet wavelet transform (DWT), and calculates the entropy as features. Then, a K-ELM is trained to classify images as pathological or healthy. A 10 × 10-fold cross validation is conducted to prevent overfitting. The method achieves the sensitivity as 97.48 %, the specificity as 94.44 %, and the overall accuracy as 97.04 % based on 125 MR images. The performance suggests the classifier is robust and effective by comparison with the recently published approaches.


Wavelet entropy K-ELM Classification Pattern recognition 



This study is financially supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Natural Science Foundation of Jiangsu Province (BK20150983), Open Project Program of the State Key Lab of CAD&CG, Zhejiang University (A1616), the Fundamental Research Funds for the Central Universities (LGYB201604)

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest involved in this paper.


  1. 1.
    Aguiar V, Guedes I (2015) Shannon entropy, Fisher information and uncertainty relations for log-periodic oscillators. Phys A: Stat Mech Appl 423:72–79MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bamford C, Olsen K, Davison C, Barnett N, Lloyd J, Williams D, Firbank M, Mason H, Donaldson C, O’Brien J (2016) Is there a preference for PET or SPECT brain imaging in diagnosing dementia? The views of people with dementia, carers, and healthy controls. Int Psychogeriatr 28(1):123–131CrossRefGoogle Scholar
  3. 3.
    Cao WB, Ma JS, Su P, Liang XT (2016) Binary hologram generation based on discrete wavelet transform. Optik 127(2):558–561CrossRefGoogle Scholar
  4. 4.
    Chen Y, Shi L, Feng Q, Yang J, Shu H (2014) Artifact suppressed dictionary learning for low-dose CT image processing. IEEE Trans Med Imaging 33(12):2271–2292CrossRefGoogle Scholar
  5. 5.
    Chen Y, Yang J, Cao Q, Yang G, Chen J, Shu H, Luo L, Coatrieux J-L, Feng Q (2016) Curve-like structure extraction using minimal path propagation with back-tracing. IEEE Trans Image Process 25(2):988–1003MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chen Y, Yin X, Shi L (2013) Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing. Phys Med Biol 58(16):5803–5820CrossRefGoogle Scholar
  7. 7.
    Dong Z, 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 (GEPSVM). Entropy 17(4):1795–1813Google Scholar
  8. 8.
    Dong Z, Phillips P, Wang S, Ji G, Yang J, Yuan T-f (2015) Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front Comput Neurosci 66(9):1–15Google Scholar
  9. 9.
    El-Dahshan ESA, Hosny T, Salem ABM (2010) Hybrid intelligent techniques for MRI brain images classification. Digital Signal Process 20(2):433–441CrossRefGoogle Scholar
  10. 10.
    Ertugrul OF (2016) Forecasting electricity load by a novel recurrent extreme learning machines approach. Int J Electr Power Energy Syst 78:429–435CrossRefGoogle Scholar
  11. 11.
    Fallah M, Modarresi J, Ajami A, Bina MT (2016) Improvement of indirect harmonic compensation method using online discrete wavelet transform. J Circuits Syst Comput 25(4):20CrossRefGoogle Scholar
  12. 12.
    Harikumar R, Kumar BV (2015) Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. Int J Imaging Syst Technol 25(1):33–40CrossRefGoogle Scholar
  13. 13.
    He YL, Geng ZQ, Zhu QX (2016) Soft sensor development for the key variables of complex chemical processes using a novel robust bagging nonlinear model integrating improved extreme learning machine with partial least square. Chemom Intell Lab Syst 151:78–88CrossRefGoogle Scholar
  14. 14.
    Hu CH, Sepulcre J, Johnson KA, Fakhri GE, Lu YM, Li QZ (2016) Matched signal detection on graphs: theory and application to brain imaging data classification. Neuroimage 125:587–600CrossRefGoogle Scholar
  15. 15.
    Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70(16–18):3056–3062CrossRefGoogle Scholar
  16. 16.
    Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501CrossRefGoogle Scholar
  17. 17.
    Huo Y, Wu L (2010) Feature extraction of brain MRI by stationary wavelet transform and its applications. J Biol Syst 18(1):115–132Google Scholar
  18. 18.
    Hwang M, Song JS, Lee YS, Li C, Shim EB, Pak HN (2016) Electrophysiological rotor ablation in in-silico modeling of atrial fibrillation: comparisons with dominant frequency, Shannon entropy, and phase singularity. Plos One 11(2):15Google Scholar
  19. 19.
    Kumar A, Singh M (2015) Optimal selection of wavelet function and decomposition level for removal of ECG signal artifacts. J Med Imaging Health Inf 5(1):138–146CrossRefGoogle Scholar
  20. 20.
    Kushnirsky M, Nguyen V, Katz JS, Steinklein J, Rosen L, Warshall C, Schulder M, Knisely JPS (2016) Time-delayed contrast-enhanced MRI improves detection of brain metastases and apparent treatment volumes. J Neurosurg 124(2):489–495CrossRefGoogle Scholar
  21. 21.
    Li B, Rong XW, Li YB (2014) An improved kernel based extreme learning machine for robot execution failures. Sci World J 7Google Scholar
  22. 22.
    Liu G, Phillips P, Yuan T-F (2016) Detection of Alzheimer’s disease by three-dimensional displacement field estimation in structural magnetic resonance imaging. J Alzheimers Dis 50(1):233–248Google Scholar
  23. 23.
    Ma C, Ouyang JH, Guan, J (2014) Hybrid improved Gravitional search algorithm and kernel based extreme learning machine method for classification problems. 2014 International Conference on Security, Pattern Analysis, and Cybernetics (Spac) 299–304Google Scholar
  24. 24.
    Maitra M, Chatterjee A (2006) A Slantlet transform based intelligent system for magnetic resonance brain image classification. Biomed Signal Process Control 1(4):299–306CrossRefGoogle Scholar
  25. 25.
    Mondal U, Sengupta A, Pathak RR (2016) Servomechanism for periodic reference input: Discrete wavelet transform-based repetitive controller. Trans Inst Meas Control 38(1):14–22CrossRefGoogle Scholar
  26. 26.
    Mulia IE, Asano T, Nagayama A (2016) Real-time forecasting of near-field tsunami waveforms at coastal areas using a regularized extreme learning machine. Coast Eng 109:1–8CrossRefGoogle Scholar
  27. 27.
    Saber A, Emam A, Amer R (2016) Discrete wavelet transform and support vector machine-based parallel transmission line faults classification. IEEJ Trans Electr Electron Eng 11(1):43–48CrossRefGoogle Scholar
  28. 28.
    Shamshirband S, Mohammadi K, Tong CW, Petkovic D, Porcu E, Mostafaeipour A, Ch S, Sedaghat A (2016) Application of extreme learning machine for estimation of wind speed distribution. Clim Dyn 46(5–6):1893–1907CrossRefGoogle Scholar
  29. 29.
    Sudeb D, Manish C, Malay KK (2013) Brain Mr image classification using multiscale geometric analysis of Ripplet. Prog Electromagn Res 137:1–17CrossRefGoogle Scholar
  30. 30.
    Tang J, Deng C, Huang GB (2015) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn SystGoogle Scholar
  31. 31.
    Wang S, Dong Z, Du S, Ji G, Yan J, Yang J, Wang Q, Feng C, Phillips P (2015) Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int J Imaging Syst Technol 25(2):153–164CrossRefGoogle Scholar
  32. 32.
    Wang S, Du S, Atangana A, Liu A, Lu Z (2016) Application of stationary wavelet entropy in pathological brain detection. Multimed Tools Appl 1–14Google Scholar
  33. 33.
    Wang B, Huang S, Qiu J, Liu Y, Wang G (2015) Parallel online sequential extreme learning machine based on MapReduce. Neurocomputing 149:224–232CrossRefGoogle Scholar
  34. 34.
    West RJH, Elliott CJH, Wade AR (2015) Classification of Parkinson’s disease genotypes in drosophila using spatiotemporal profiling of vision. Sci Rep 5:13CrossRefGoogle Scholar
  35. 35.
    Wong PK, Wong KI, Vong CM, Cheung CS (2015) Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search. Renew Energy 74:640–647CrossRefGoogle Scholar
  36. 36.
    Yamashita Y, Wakahara T (2016) Affine-transformation and 2D-projection invariant k-NN classification of handwritten characters via a new matching measure. Pattern Recogn 52:459–470CrossRefGoogle Scholar
  37. 37.
    Yang G, Zhang Y, Yang J, Ji G, Dong Z, Wang S, Feng C, Wang Q (2015) Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimed Tools Appl, 1–17Google Scholar
  38. 38.
    Yaroshenko TY, Krysko DV, Dobriyan V, Zhigalov MV, Vos H, Vandenabeele P, Krysko VA (2015) Wavelet modeling and prediction of the stability of states: the Roman Empire and the European Union. Commun Nonlinear Sci Numer Simul 26(1–3):265–275MathSciNetCrossRefGoogle Scholar
  39. 39.
    Yuvaraj R, Murugappan M, Acharya UR, Adeli H, Ibrahim NM, Mesquita E (2016) Brain functional connectivity patterns for emotional state classification in Parkinson’s disease patients without dementia. Behav Brain Res 298:248–260CrossRefGoogle Scholar
  40. 40.
    Zhang Y, Dong Z, Ji G (2015) Effect of spider-web-plot in MR brain image classification. Pattern Recogn Lett 62:14–16CrossRefGoogle Scholar
  41. 41.
    Zhang Y, Wang S (2015) Detection of Alzheimer’s disease by displacement field and machine learning. Peer J 3Google Scholar
  42. 42.
    Zhang Y, Wang S, Dong Z, Phillips P, Ji G, Yang J (2015) Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization. Prog Electromagn Res 152:41–58CrossRefGoogle Scholar
  43. 43.
    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 2013:9Google Scholar
  44. 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. TheScientificWorldJOURNAL 2013:130134Google Scholar
  45. 45.
    Zhang Y, Wang S, Phillips P, Dong Z, Ji G, Yang J (2015) Detection of Alzheimer’s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC. Biomed Signal Process Control 21:58–73CrossRefGoogle Scholar
  46. 46.
    Zhang Y, Wang S, Phillips P, Yang J, Yuan T-F (2016) Three-dimensional Eigenbrain for the detection of subjects and brain regions related with Alzheimer’s disease. J Alzheimers Dis 50(4):1163–1179CrossRefGoogle Scholar
  47. 47.
    Zhang Y, Wu L (2012) An Mr brain images classifier via principal component analysis and kernel support vector machine. Prog Electromagn Res 130:269–388Google Scholar
  48. 48.
    Zhang Y, Yang X, Cattani C, Rao R, Wang S, Phillips P (2016) Tea category identification using a novel fractional Fourier entropy and Jaya algorithm. Entropy 18(3):77CrossRefGoogle Scholar
  49. 49.
    Zhou X, Wang S, Xu W, Ji G, Phillips P, Sun P, Zhang Y (2015) Detection of pathological brain in MRI scanning based on wavelet-entropy and naive Bayes classifier. In: Ortuño F, Rojas I (eds) Bioinformatics and biomedical engineering, vol 9043. Springer International Publishing, Granada, pp 201–209Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Siyuan Lu
    • 1
    • 2
  • Zhihai Lu
    • 1
    • 2
  • Jianfei Yang
    • 3
  • Ming Yang
    • 4
  • Shuihua Wang
    • 1
    • 5
    • 6
  1. 1.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  2. 2.School of Education ScienceNanjing Normal UniversityNanjingChina
  3. 3.Jiangsu Key Laboratory of 3D Printing Equipment and ManufacturingNanjingChina
  4. 4.Department of Radiology, Nanjing Children’s HospitalNanjing Medical UniversityNanjingChina
  5. 5.State Key Lab of CAD & CGZhejiang UniversityHangzhouChina
  6. 6.Department of Electrical EngineeringThe City College of New York, CUNYNew YorkUSA

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