Preliminary research on abnormal brain detection by wavelet-energy and quantum-behaved PSO

Yudong Zhang*, Genlin Ji, Jiquan Yang, Shuihua Wang, Zhengchao Dong, Preetha Phillips, Ping Sun

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

43 Citations (Scopus)


It is important to detect abnormal brains accurately and early. The wavelet-energy (WE) was a successful feature descriptor that achieved excellent performance in various applications; hence, we proposed a WE based new approach for automated abnormal detection, and reported its preliminary results in this study. The kernel support vector machine (KSVM) was used as the classifier, and quantum-behaved particle swarm optimization (QPSO) was introduced to optimize the weights of the S VM. The results based on a 5 × 5-fold cross validation showed the performance of the proposed WE + QPSO-KSVM was superior to "DWT + PCA + BP-NN", "DWT + PCA + RBF-NN", "DWT + PCA + PSO-KSVM", "WE + BPNN", "WE + KSVM", and "DWT + PCA + GA-KSVM" w.r.t. sensitivity, specificity, and accuracy. The work provides a novel means to detect abnormal brains with excellent performance.

Original languageEnglish
Pages (from-to)S641-S649
JournalTechnology and Health Care
Publication statusPublished - 13 Jun 2016
Externally publishedYes
Event4th International Conference on Biomedical Engineering and Biotechnology, iCBEB 2015 - Shanghai, China
Duration: 18 Aug 201521 Aug 2015


  • Magnetic resonance imaging
  • Particle swarm optimization
  • Quantum-behaved PSO
  • Wavelet energy


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