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
Article

Abstract

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.

Keywords

Wavelet entropy K-ELM Classification Pattern recognition 

Notes

Acknowledgments

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.

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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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