Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm
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.
KeywordsPathological brain detection Synthetic minority oversampling Extreme learning machine Jaya algorithm
The paper is supported by Natural Science Foundation of China (61602250), Natural Science Foundation of Jiangsu Province (BK20150983), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL1601).
Y Zhang & G Zhao conceived the study. J Sun & T Zhan designed the model. Y Zhang & J Li acquired the data. Y Zhang, T Zhan, J Li analyzed the data. G Zhao interpreted the data. Y Zhang, X Wu, Z Wang, H Liu, V Govindaraj developed the programs. Y Zhang & T Zhan wrote the draft. All the authors gave critical revisions and approved the submission.
Compliance with ethical standards
Conflict of interest
We declared there is no conflict of interest in terms with this submission.
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