Automated classification of brain images using wavelet-energy and biogeography-based optimization

Gelan Yang, Yudong Zhang*, Jiquan Yang, Genlin Ji, Zhengchao Dong, Shuihua Wang, Chunmei Feng, Qiong Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

147 Citations (Scopus)


It is very important to early detect abnormal brains, in order to save social and hospital resources. The wavelet-energy was a successful feature descriptor that achieved excellent performances in various applications; hence, we proposed a novel wavelet-energy based approach for automated classification of MR brain images as normal or abnormal. SVM was used as the classifier, and biogeography-based optimization (BBO) was introduced to optimize the weights of the SVM. The results based on a 5 × 5-fold cross validation showed the performance of the proposed BBO-KSVM was superior to BP-NN, KSVM, and PSO-KSVM in terms of sensitivity and accuracy. The study offered a new means to detect abnormal brains with excellent performance.

Original languageEnglish
Pages (from-to)15601-15617
Number of pages17
JournalMultimedia Tools and Applications
Issue number23
Publication statusPublished - 1 Dec 2016
Externally publishedYes


  • Biogeography-based optimization
  • Classification
  • Magnetic resonance imaging
  • Pattern recognition
  • Support vector machine


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