Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm - Zhang, Yu-Dong; Zhao, Guihu; Sun, Junding; Wu, Xiaosheng; Wang, Zhi-Heng; Liu, Hong-Min; Govindaraj, Vishnu Varthanan; Zhan, Tianmin; Li, Jianwu (Multimedia tools and applications, 2018)
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.