Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 77, Issue 17, Pages 22629-22648Publisher
SPRINGER
DOI: 10.1007/s11042-017-5023-0
Keywords
Pathological brain detection; Synthetic minority oversampling; Extreme learning machine; Jaya algorithm
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Funding
- Natural Science Foundation of China [61602250]
- Natural Science Foundation of Jiangsu Province [BK20150983]
- Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence [2016CSCI01]
- Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology [HGAMTL1601]
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Pathological brain detection is an automated computer-aided diagnosis for brain images. This study provides a novel method to achieve this goal.We first used synthetic minority oversampling to balance the dataset. Then, our system was based on three components: wavelet packet Tsallis entropy, extreme learning machine, and Jaya algorithm. The 10 repetitions of K-fold cross validation showed our method achieved perfect classification on two small datasets, and achieved a sensitivity of 99.64 +/- 0.52%, a specificity of 99.14 +/- 1.93%, and an accuracy of 99.57 +/- 0.57% over a 255-image dataset. Our method performs better than six state-of-the-art approaches. Besides, Jaya algorithm performs better than genetic algorithm, particle swarm optimization, and bat algorithm as ELM training method.
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