Cerebral Microbleed Detection by Wavelet Entropy and Naive Bayes Classifier

作者: Hai-nan WANG , Beatrice Gagnon

DOI: 10.2991/BBE-17.2017.81

关键词:

摘要:

参考文章(37)
Yudong Zhang, Shuihua Wang, Ping Sun, Preetha Phillips, Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-medical Materials and Engineering. ,vol. 26, ,(2015) , 10.3233/BME-151426
Shuihua Wang, Xiaojun Yang, Yudong Zhang, Preetha Phillips, Jianfei Yang, Ti-Fei Yuan, Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine Entropy. ,vol. 17, pp. 6663- 6682 ,(2015) , 10.3390/E17106663
Yudong Zhang, Shuihua Wang, Genlin Ji, Preetha Phillips, Fruit classification using computer vision and feedforward neural network Journal of Food Engineering. ,vol. 143, pp. 167- 177 ,(2014) , 10.1016/J.JFOODENG.2014.07.001
Yudong Zhang, Lenan Wu, Geng Wei, Shuihua Wang, A novel algorithm for all pairs shortest path problem based on matrix multiplication and pulse coupled neural network Digital Signal Processing. ,vol. 21, pp. 517- 521 ,(2011) , 10.1016/J.DSP.2011.02.004
Yudong Zhang, Lenan Wu, Shuihua Wang, MAGNETIC RESONANCE BRAIN IMAGE CLASSIFICATION BY AN IMPROVED ARTIFICIAL BEE COLONY ALGORITHM Progress in Electromagnetics Research-pier. ,vol. 116, pp. 65- 79 ,(2011) , 10.2528/PIER11031709
Yudong Zhang, Lenan Wu, Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network Expert Systems With Applications. ,vol. 36, pp. 8849- 8854 ,(2009) , 10.1016/J.ESWA.2008.11.028
Yudong Zhang, Lenan Wu, An Mr Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine Progress in Electromagnetics Research-pier. ,vol. 130, pp. 369- 388 ,(2012) , 10.2528/PIER12061410
Yudong Zhang, Shuihua Wang, Lenan Wu, A Novel Method for Magnetic Resonance Brain Image Classification Based on Adaptive Chaotic PSO Progress in Electromagnetics Research-pier. ,vol. 109, pp. 325- 343 ,(2010) , 10.2528/PIER10090105
Yudong Zhang, Lenan Wu, Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine Sensors. ,vol. 12, pp. 12489- 12505 ,(2012) , 10.3390/S120912489