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- Title
Automated classification of brain images using wavelet-energy and biogeography-based optimization.
- Authors
Yang, Gelan; Zhang, Yudong; Yang, Jiquan; Ji, Genlin; Dong, Zhengchao; Wang, Shuihua; Feng, Chunmei; Wang, Qiong
- Abstract
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.
- Subjects
BRAIN abnormalities; MAGNETIC resonance imaging; WAVELETS (Mathematics); PATTERN perception; PATTERN recognition systems; DIAGNOSTIC imaging
- Publication
Multimedia Tools & Applications, 2016, Vol 75, Issue 23, p15601
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-015-2649-7