Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

IAPR International Conference on Pattern Recognition in Bioinformatics

PRIB 2012: Pattern Recognition in Bioinformatics pp 94–105Cite as

  1. Home
  2. Pattern Recognition in Bioinformatics
  3. Conference paper
A Novel Machine Learning Approach for Detecting the Brain Abnormalities from MRI Structural Images

A Novel Machine Learning Approach for Detecting the Brain Abnormalities from MRI Structural Images

  • Lavneet Singh23,
  • Girija Chetty23 &
  • Dharmendra Sharma23 
  • Conference paper
  • 2082 Accesses

  • 22 Citations

Part of the Lecture Notes in Computer Science book series (LNBI,volume 7632)

Abstract

In this study, we present the investigations being pursued in our research laboratory on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images. We propose a novel method based ondeep and extreme machine learning on wavelet transform to initially decompose the images, and then use various features selection and search algorithms to extract the most significant features of brain from the MRI images. By using a comparative study with different classifiers to detect the abnormality of brain images from publicly available neuro-imaging dataset, we found that a principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.

Keywords

  • Deep Machine Learning
  • Extreme Machine Learning
  • MRI
  • PCA

Download conference paper PDF

References

  1. Fletcher-Heath, L.M., Hall, L.O., Goldgof, D.B., Murtagh, F.R.: Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artificial Intelligencein Medicine 21, 43–63 (2001)

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  3. Gorunescu, F.: Data Mining Techniques in Computer-Aided Diagnosis: Non-InvasiveCancer Detection. PWASET 25, 427–430 (2007) ISSN 1307-6884

    Google Scholar 

  4. Kara, S., Dirgenali, F.: A system to diagnose atherosclerosis via wavelet transforms,principal component analysis and artificial neural networks. Expert Systems with Applications 32, 632–640 (2007)

    CrossRef  Google Scholar 

  5. Maitra, M., Chatterjee, A.: Hybrid multi-resolutionSlantlet transform and fuzzy c-means clustering approach for normal-pathological brain MR image segregation. Med. Eng. Phys. (2007), doi:10.1016/j.medengphy.2007.06.009

    Google Scholar 

  6. Abdolmaleki, P., Mihara, F., Masuda, K., DansoBuadu, L.: Neural networks analysis of astrocyticgliomas from MRI appearances. Cancer Letters 118, 69–78 (1997)

    CrossRef  Google Scholar 

  7. Rosenbaum, T., Engelbrecht, V., Krolls, W., van Dorstenc, F.A., Hoehn-Berlagec, M., Lenard, H.: MRI abnormalities in neuro-bromatosistype 1 (NF1): a study of men and mice. Brain & Development 21, 268–273 (1999)

    CrossRef  Google Scholar 

  8. Cocosco, C., Zijdenbos, A.P., Evans, A.C.: A fully automatic and robust brainMRI tissue classification method. Medical Image Analysis 7, 513–527 (2003)

    CrossRef  Google Scholar 

  9. Database taken from, http://med.harvard.edu/AANLIB/

  10. Hintonand, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    CrossRef  MathSciNet  Google Scholar 

  11. Hinton, G.E., Osindero, S.: A fast learning algorithm for deep belief nets. Neural Computation 18, 1527–1554 (2006)

    CrossRef  MathSciNet  MATH  Google Scholar 

  12. Lin, M.-B., Huang, G.-B., Saratchandran, P., Sudararajan, N.: Fully complex extreme learning machine. Neurocomputing 68, 306–314 (2005)

    CrossRef  Google Scholar 

  13. Huang, G.-B., Zhu, Q.-Y., Siew, C.K.: Extreme Learning Machine: Theory and Applications. Neurocomputing 70, 489–501 (2006)

    CrossRef  Google Scholar 

  14. Serre, D.: Matrices: Theory and Applications. Springer Verlag, New York Inc. (2002)

    Google Scholar 

  15. Mishra, A., Singh, L., Chetty, G.: A Novel Image Water Marking Scheme Using Extreme Learning Machine. In: Proceedings of IEEE World Congress on Computational Intelligence (WCCI 2012). IEEE Explore, Brisbane (2012)

    Google Scholar 

  16. Singh, L., Chetty, G., Sharma, D.: A Hybrid Approach to Increase the Performance of Protein Folding Recognition Using Support Vector Machines. In: Perner, P. (ed.) MLDM 2012. LNCS, vol. 7376, pp. 660–668. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  17. Singh, L., Chetty, G.: Review of Classification of Brain Abnormalities in Magnetic Resonance Images Using Pattern Recognition and Machine Learning. In: Proceedings of International Conference of Neuro Computing and Evolving Intelligence, NCEI 2012, Auckland, New-Zealand. LNCS Bioinformatics, Springer (2012)

    Google Scholar 

  18. Singh, L., Chetty, G.: A Novel Approach for protein Structure prediction Using Pattern Recognition and Extreme Machine Learning. In: Proceedings of International Conference of Neuro Computing and Evolving Intelligence, NCEI 2012, Auckland, New-Zealand. LNCS Bioinformatics. Springer (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Information Sciences and Engineering, University of Canberra, Australia

    Lavneet Singh, Girija Chetty & Dharmendra Sharma

Authors
  1. Lavneet Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Girija Chetty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dharmendra Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Medical Science, University of Tokyo, 4-6-1, Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan

    Tetsuo Shibuya

  2. Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, 113-8654, Bunkyo-ku, Tokyo, Japan

    Hisashi Kashima

  3. Department of Comouter Science, Tokyo Institute of Technology, 2-12-1 Ookayamama, 152-8550, Meguro-ku, Tokyo, Japan

    Jun Sese

  4. Bioinformatics Project, National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, 567-0085, Suita, Osaka, Japan

    Shandar Ahmad

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Singh, L., Chetty, G., Sharma, D. (2012). A Novel Machine Learning Approach for Detecting the Brain Abnormalities from MRI Structural Images. In: Shibuya, T., Kashima, H., Sese, J., Ahmad, S. (eds) Pattern Recognition in Bioinformatics. PRIB 2012. Lecture Notes in Computer Science(), vol 7632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34123-6_9

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-34123-6_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34122-9

  • Online ISBN: 978-3-642-34123-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • The International Association for Pattern Recognition

    Published in cooperation with

    http://www.iapr.org/

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature