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Progress In Electromagnetics Research
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PATHOLOGICAL BRAIN DETECTION BY ARTIFICIAL INTELLIGENCE IN MAGNETIC RESONANCE IMAGING SCANNING (INVITED REVIEW)

By S. Wang, Y. Zhang, T. Zhan, P. Phillips, Y.-D. Zhang, G. Liu, S. Lu, and X. Wu

Full Article PDF (787 KB)

Abstract:
(Aim) Pathological brain detection (PBD) systems aim to assist and even replace neuroradiologists to make decisions for patients. This review offers a comprehensive and quantitative comparison for PBD systems by artificial intelligence in magnetic resonance imaging (MRI) scanning. (Method) We first investigated four categories of brain diseases, including neoplastic disease, neurodegenerative disease, cerebrovascular disease, and inflammation. Next, we introduced important MRI techniques, such as the shimming, water and fat suppression, and three advanced imaging modalities (functional MRI, diffusion tensor imaging, and magnetic resonance spectroscopic imaging). Then, we discussed four image preprocessing techniques (image denoising, slice selection, brain extraction, spatial normalization, and intensity normalization), seven feature representation techniques (shape, moment, wavelet, statistics, entropy, gray level co-occurrence matrix, and Fourier transform), and two dimension reduction techniques (feature selection and feature extraction). Afterwards, we studied classification related methods: six learning models (decision tree, extreme learning machine, k-nearest neighbors, naive Bayes classifier, support vector machine, feed-forward neural network), five kernel functions (linear, homogeneous and inhomogeneous polynomial, radial basis function, and sigmoid), and three types of optimization methods (evolutionary algorithm, stochastic optimization, and swarm intelligence). (Results) We introduced three benchmark datasets and used Kfold stratified cross validation to avoid overfitting. We presented a detailed quantitative comparison among 44 state-of-the-art PBD algorithms and discussed their advantages and limitations. (Discussions) Artificial intelligence is now making stride in the PBD field and enjoys a fair amount of success. In the future, semi-supervised learning and transfer learning techniques may be potential breakthroughs to develop PBD systems.

Citation:
S. Wang, Y. Zhang, T. Zhan, P. Phillips, Y.-D. Zhang, G. Liu, S. Lu, and X. Wu, "Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)," Progress In Electromagnetics Research, Vol. 156, 105-133, 2016.
doi:10.2528/PIER16070801
http://www.jpier.org/PIER/pier.php?paper=16070801

References:
1. Gnecco, G., J. Optim. Theory Appl., Vol. 168, 488, 2016.

2. Mori, C., et al., Amyloid Precursor Protein: A Practical Approach, W. Xia, et al. (eds.), 165, CRC Press-Taylor & Francis Group, 2005.

3. Wain, R. A., et al., Symposium on Computer Applications in Medical Care, 94, 1991.

4. Lloret, R. L., et al., American Heart Journal, Vol. 110, 761, 1985.

5. Cavestri, R., et al., Minerva Medica, Vol. 82, 815, 1991.

6. Brai, A., et al., Computers and Biomedical Research, Vol. 27, 351, 1994.

7. Juhola, M., et al., Medical Informatics, Vol. 20, 133, 1995.

8. Imran, M. B., et al., Nuclear Medicine Communications, Vol. 20, 25, 1999.

9. Terae, S., et al., International Congress Series, 459, H. U. Lemke, et al. (eds.), Elsevier Science Publishers, 1998.

10. Barra, V., et al., JMRI --- Journal of Magnetic Resonance Imaging, Vol. 11, 267, 2000.

11. Antel, S. B., et al., Epilepsia, Vol. 44, 255, 2003.

12. Coulon, D., et al., IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 1, 94, 1979.

13. Bartels, P. H., et al., Acta Cytologica, Vol. 18, 376, 1974.

14. Bonettini, S., et al., Appl. Math. Comput., Vol. 286, 288, 2016.

15. Dorigo, M., Politecnico di Milano, Italy, 1992.

16. Kennedy, J., et al., IEEE International Conference on Neural Networks, 1942, IEEE, 1995.

17. Storn, R., et al., Journal of Global Optimization, Vol. 11, 341, 1997.

18. Karaboga, D., et al., Foundations of Fuzzy Logic and Soft Computing, P. Melin, et al. (eds.), 789, Springer-Verlag Press, Berlin, 2007.

19. Krishnanand, K. N., et al., IEEE Swarm Intelligence Symposium, 84, IEEE, Pasadena, 2005.

20. Gaviani, P., et al., Neurol. Sci., Vol. 34, 2151, 2013.

21. Chandra, S., et al., J. Neuro-Oncol., Vol. 127, 33, 2016.

22. Matsunaga, S., et al., World Neurosurg., Vol. 89, 455, 2016.

23. Sands, S. A., J. Clin. Oncol., Vol. 34, 1024, 2016.

24. Rathe, M., et al., Chemotherapy, Vol. 61, 204, 2015.

25. Martinez-Gonzalez, A., et al., Mathematical Medicine and Biology, Vol. 32, 239, 2015.

26. Hou, J. X., et al., CNS Neurosci. Ther., Vol. 22, 244, 2016.

27. Chanda, S., et al., Proc. Natl. Acad. Sci. U.S.A., Vol. 110, 16622, 2013.

28. Hanif, U., et al., 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,, 6002, IEEE, Milan, Italy, 2015.

29. Menezes, R., et al., Medicine, Vol. 94, e971, 2015.

30. Srulijes, K., et al., BMC Neurol., Vol. 15, 192, 2015.

31. Kulminski, A. M., et al., Rejuv. Res., Vol. 18, 128, 2015.

32. Anderson, K. N., et al., Nature and Science of Sleep, Vol. 5, 61, 2013.

33. Martinez-Perez, B., et al., Health Inform. J., Vol. 21, 267, 2015.

34. Stone, N. J., et al., Mayo Clinic Proceedings, Vol. 91, 692, 2016.

35. Everson-Rose, S. A., et al., Stroke, Vol. 45, 2318, 2014.

36. Spurgeon, L., et al., Psychol. Health Med., Vol. 21, 632, 2016.

37. Yolas, C., et al., World Neurosurg., Vol. 86, 79, 2016.

38. Akanuma, K., et al., J. Clin. Neurosci., Vol. 28, 128, 2016.

39. Troletti, C. D., et al., Biochim. Biophys. Acta-Mol. Basis Dis., Vol. 1862, 452, 2016.

40. Slone, H. W., et al., Clin. Imaging, Vol. 37, 361, 2013.

41. Shyu, L. Y., et al., Zoonoses Public Health, Vol. 61, 411, 2014.

42. Thomas, A. C., et al., Clin. Nucl. Med., Vol. 40, 358, 2015.

43. Gonzalez-Suarez, I., et al., Cerebrovasc. Dis., Vol. 41, 313, 2016.

44. Sadatsafavi, M., et al., Allergy, Vol. 71, 371, 2016.

45. Rerich, E., et al., NMR in Biomedicine, Vol. 28, 1402, 2015.

46. Pooley, R. A., et al., American Journal of Roentgenology, Vol. 206, 230, 2016.

47. Grupe, G., et al., HNO, Vol. 64, 156, 2016.

48. Sykora, M., et al., Ceska a Slovenska Neurologie a Neurochirurgie, Vol. 71, 47, 2008.

49. Mattei, E., et al., PIERS Proceedings, 639-642, Marrakesh, Morocco, Mar. 20-23, 2011.

50. Muhlenweg, M., et al., Radiologe, Vol. 55, 638, 2015.

51. Brink, W. M., et al., Magn. Reson. Med., Vol. 75, 2185, 2016.

52. Schneider, R., et al., Magn. Reson. Med., Vol. 74, 934, 2015.

53. Ahn, M. C., et al., IEEE Trans. Appl. Supercond., Vol. 25, 4300804, 2015.

54. Kong, X., et al., J. Magn. Reson., Vol. 263, 122, 2016.

55. Toth, J., et al., IEEE Trans. Appl. Supercond., Vol. 23, 4300104, 2013.

56. Giraudeau, P., et al., Metabolomics, Vol. 11, 1041, 2015.

57. Tagliafico, A., et al., Radiologia Medica, Vol. 121, 45, 2016.

58. Van der Velden, T. A., et al., Magn. Reson. Imaging, Vol. 34, 462, 2016.

59. Goez, M., et al., J. Magn. Reson., Vol. 177, 236, 2005.

60. Lippens, G., et al., Journal of Biomolecular NMR, Vol. 5, 327, 1995.

61. Louis-Joseph, A., et al., Chem. Phys. Lett., Vol. 337, 92, 2001.

62. https://mriatmrc.com/fb/body-mri/fat-suppression-techniques/, 2013.

63. Kobayashi, T., et al., Magn. Reson. Med. Sci., Vol. 13, 67, 2014.

64. Guerini, H., et al., Semin. Musculoskelet. Radiol., Vol. 19, 335, 2015.

65. Deligianni, X., et al., Magn. Reson. Med., Vol. 72, 800, 2014.

66. Clauser, P., et al., Eur. Radiol., Vol. 24, 2213, 2014.

67. Choi, W. H., et al., Korean Journal of Spine, Vol. 9, 227, 2012.

68. Brier, M. R., et al., Journal of Neurology, Vol. 263, 1083, 2016.

69. Markuerkiaga, I., et al., Neuroimage, Vol. 132, 491, 2016.

70. Weerakoon, B. S., et al., Appl. Magn. Reson., Vol. 47, 453, 2016.

71. Xiong, J., et al., Eur. Radiol., Vol. 26, 1705, 2016.

72. Taso, M., et al., NMR in Biomedicine, Vol. 29, 817, 2016.

73. Chen, G. X., et al., Sci. Rep., Vol. 6, 21825, 2016.

74. Sparacia, G., et al., Neuradiology J., Vol. 29, 160, 2016.

75. Lee, S. J., et al., Appl. Magn. Reson., Vol. 45, 1333, 2014.

76. Fuchs, J., et al., Magn. Reson. Mat. Phys. Biol. Med., Vol. 28, 127, 2015.

77. Whiteley, W. N., et al., Neurology, Vol. 79, 152, 2012.

78. Goh, S., et al., JAMA Psychiatry, Vol. 71, 665, 2014.

79. Belkic, D., et al., J. Math. Chem., Vol. 54, 602, 2016.

80. Fleischer, V., et al., Mult. Scler. J., Vol. 20, 310, 2014.

81. O’Neill, J., et al., Epilepsia, Vol. 52, 1705, 2011.

82. Harper, D. G., et al., Am. J. Geriatr. Psychiatr., Vol. 22, 499, 2014.

83. Ciurleo, R., et al., Neurosci. Lett., Vol. 599, 55, 2015.

84. Wang, J., et al., J. Craniofac. Surg., Vol. 25, 2147, 2014.

85. Shi, L. Y., et al., Fluct. Noise Lett., Vol. 14, 1550002, 2015.

86. Zhang, Y., et al., Science in China Series F: Information Sciences, Vol. 51, 2115, 2008.

87. Iftikhar, M. A., et al., Int. J. Imaging Syst. Technol., Vol. 24, 52, 2014.

88. Phophalia, A., et al., Signal Process., Vol. 103, 24, 2014.

89. Yang, J., et al., Biomed. Eng. Online, Vol. 14, 2, 2015.

90. Phophalia, A., et al., Appl. Soft. Comput., Vol. 33, 1, 2015.

91. Akar, S. A., Appl. Soft. Comput., Vol. 43, 87, 2016.

92. Mirsadraee, S., et al., Quant. Imaging Med. Surg., Vol. 6, 42, 2016.

93. Middione, M. J., et al., Magn. Reson. Med., Vol. 71, 2014, 2014.

94. Yuan, T. F., et al., Frontiers in Computational Neuroscience, Vol. 9, 66, 2015.

95. Kalavathi, P., et al., Journal of Digital Imaging, Vol. 29, 365, 2016.

96. Smith, S. M., Hum. Brain Mapp., Vol. 17, 143, 2002.

97. Doshi, J., et al., Acad. Radiol., Vol. 20, 1566, 2013.

98. Roura, E., et al., Comput. Meth. Programs Biomed., Vol. 113, 655, 2014.

99. Prasad, G., et al., J. Neurosci. Methods, Vol. 236, 114, 2014.

100. Moldovanu, S., et al., Journal of Digital Imaging, Vol. 28, 738, 2015.

101. Kleesiek, J., et al., Neuroimage, Vol. 129, 460, 2016.

102. Alansary, A., et al., IEEE J. Biomed. Health Inform., Vol. 20, 925, 2016.

103. Kronfeld, A., et al., Med. Phys., Vol. 42, 6875, 2015.

104. Shattuck, D. W., et al., Neuroimage, Vol. 39, 1064, 2008.

105. Jenkinson, M., et al., Neuroimage, Vol. 17, 825, 2002.

106. Lancaster, J. L., et al., Neuroinformatics, Vol. 8, 171, 2010.

107. Rorden, C., et al., Neuroimage, Vol. 61, 957, 2012.

108. Li, X. F., et al., PLoS One, Vol. 9, e103302, 2014.

109. Weiss, M., et al., Brain Struct. Funct., Vol. 220, 1695, 2015.

110. Abbott, D. F., et al., Neuroimage, Vol. 44, 812, 2009.

111. Brahim, A., et al., Appl. Soft. Comput., Vol. 37, 234, 2015.

112. Babu, P., et al., Int. J. Imaging Syst. Technol., Vol. 25, 24, 2015.

113. Loizou, G. P., et al., Journal of Biomedical Graphics and Computing, Vol. 3, 20, 2013.

114. Zanier, E. R., et al., Intensive Care Medicine Experimental, Vol. 3, 39, 2015.

115. Green, T., et al., Hum. Brain Mapp., Vol. 37, 1593, 2016.

116. Jespersen, S. N., et al., NMR in Biomedicine, Vol. 26, 1647, 2013.

117. Su, Z., et al., Information Processing in Medical Imaging, Vol. 24, 411, 2015.

118. Gutierrez, J., et al., J. Am. Heart Assoc., Vol. 4, e002289, 2015.

119. Ziegel, J. F., et al., Scand. J. Stat., Vol. 42, 813, 2015.

120. Taketani, K., et al., Congenit. Anom., Vol. 55, 99, 2015.

121. Green, T., et al., Am. J. Med. Genet. B, Vol. 171, 402, 2016.

122. Santiago-Mozos, R., et al., 40th Annual Meeting on Computing in Cardiology,, 425, IEEE, Zaragoza, Spain, 2013.

123. Zaki, W., et al., J. Electron. Imaging, Vol. 19, 043021, 2010.

124. Thapaliya, K., et al., Int. J. Imaging Syst. Technol., Vol. 24, 284, 2014.

125. Gorji, H. T., et al., Neuroscience, Vol. 305, 361, 2015.

126. Hsu, Y. H. H., et al., Magn. Reson. Imaging, Vol. 31, 618, 2013.

127. Zhang, Y., et al., The Scientific World Journal, 130134, 2013.

128. Bendib, M. M., et al., Pattern Anal. Appl., Vol. 18, 829, 2015.

129. Nain, D., et al., IEEE Trans. Med. Imaging, Vol. 26, 598, 2007.

130. Perez, G., et al., Integr. Comput.-Aided Eng., Vol. 21, 163, 2014.

131. Nabizadeh, N., et al., Comput. Electr. Eng., Vol. 45, 286, 2015.

132. Renjith, A., et al., Journal of Medical Engineering & Technology, Vol. 39, 498, 2015.

133. Li, Y., et al., Magn. Reson. Med., Vol. 74, 1574, 2015.

134. Lopez-Mejia, M., et al., J. Stroke Cerebrovasc. Dis., Vol. 25, 515, 2016.

135. Tisdall, M. D., et al., Neuroimage, Vol. 127, 11, 2016.

136. Sun, Y., et al., 16th IEEE International Conference on Image Processing,, 661, B. Gimi, et al. (eds.), , IEEE, Cairo, Egypt, 2009.

137. Nardone, V., et al., Cureus, Vol. 8, e584, 2016.

138. Song, K. H., et al., J. Neurosci. Methods, Vol. 255, 75, 2015.

139. Chaudhari, A. K., et al., 3rd IEEE International Advance Computing Conference,, 1229, B. M. Kalra, et al. (eds.), IEEE, Ghaziabad, India, 2013.

140. Bhagat, M., et al., PLoS One, Vol. 4, e7173, 2009.

141. Wu, L., Sensors, Vol. 8, 7518, 2008.

142. Gao, J. H., et al., PLoS One, Vol. 6, e24446, 2011.

143. Escudero, J., et al., Brain Res. Bull., Vol. 119, 136, 2015.

144. Ferlazzo, E., et al., Clin. Neurophysiol., Vol. 125, 13, 2014.

145. Wang, X. X., et al., Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, B. Gimi, et al. (eds.), 94171N, SPIE-Int. Soc. Optical Engineering, Bellingham, 2015.

146. Frantzidis, C. A., et al., Front. Aging Neurosci., Vol. 6, 224, 2014.

147. Singh, D., et al., International Journal of Engineering and Advanced Technology, Vol. 1, 243, 2012.

148. Zvoleff, A. https://cran.r-project.org/web/packages/glcm/glcm.pdf, 2016.

149. Segovia, F., et al., Curr. Alzheimer Res., Vol. 13, 831, 2016.

150. Pham, V., et al., Annual International Conference of the IEEE Engineering in Medicine and Biology Society,, 4791, IEEE, New York, 2009.

151. Parot, V., et al., Magn. Reson. Med., Vol. 68, 17, 2012.

152. Frigo, G., et al., International Symposium on Medical Measurements And Applications (MEMEA), 313, IEEE, Lisbon, Portugal, 2014.

153. He, T., et al., Neurocomputing, Vol. 174, 1049, 2016.

154. Gouid, G. G. N., et al., IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 7, 2014.

155. Srinivasan, S. V., et al., Artificial Intelligence and Evolutionary Algorithms in Engineering Systems, Vol. 2, 687, L. P. Suresh, et al. (eds,), Springer-Verlag Berlin, Berlin, 2015.

156. Tylki-Szymanska, A., et al., Neuropediatrics, Vol. 45, 188, 2014.

157. Nardone, R., et al., J. Neuroimaging, Vol. 20, 204, 2010.

158. Canneti, B., et al., Rev. Neurologia, Vol. 57, 354, 2013.

159. Campanella, M., et al., Peer J, Vol. 2, e497, 2014.

160. Bullitt, E., et al., Methods, Vol. 43, 29, 2007.

161. Karakasis, E. G., et al., Pattern Recognit. Lett., Vol. 55, 22, 2015.

162. Bjork, G., et al., Phys. Rev. A, Vol. 85, 053835, 2012.

163. Zunic, J., et al., Mach. Vis. Appl., Vol. 27, 129, 2016.

164. Marengo, E., et al., 2-D PAGE Map Analysis: Methods and Protocols, 271, E. Marengo, et al. (eds.), Springer, New York, 2016.

165. Nayak, D. R., et al., Neurocomputing, Vol. 177, 188, 2016.

166. Wang, S., et al., 2nd National Conference on Information Technology and Computer Science (CITCS2015), 450, A. Hu (ed.), DEStech Publications, Inc., Lancaster, USA, 2015.

167. Das, A. B., et al., Signal Image Video Process., Vol. 10, 259, 2016.

168. Han, R. X., et al., Measurement Technology and Its Application, Pts. 1 and 2, 974, P. Yarlagadda, et al. (eds.), Trans Tech Publications Ltd, Stafa-Zurich, 2013.

169. Najafizade, S. A., et al., Chin. Phys. B, Vol. 25, 040301, 2016.

170. Rioul, O., et al., Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 105, A. Mohammad Djafari, et al. (eds.), Amer. Inst. Physics, Melville, 2015.

171. Mora, T., et al., Phys. Rev. E, Vol. 93, 052418, 2016.

172. Aptekarev, A. I., et al., Eur. Phys. J. B, Vol. 89, 85, 2016.

173. Senapati, D., et al., Digit. Signal Prog., Vol. 48, 276, 2016.

174. Bhandari, A. K., et al., Expert Syst. Appl., Vol. 42, 8707, 2015.

175. Mokni, R., et al., J. Inf. Assur. Secur., Vol. 11, 77, 2016.

176. Malegori, C., et al., J. Food Eng., Vol. 185, 48, 2016.

177. Yousefi Banaem, H., et al., Iranian Journal of Radiology, Vol. 12, e11656, 2015.

178. Fathima, M. M., et al., International Conference on Information Communication and Embedded Systems,, 809, IEEE, Chennai, India, 2013.

179. Haralick, R. M., et al., IEEE Transactions on Systems, Man, and Cybernetics, 610, 1973.

180. James, A. P., et al., IEEE Trans. Very Large Scale Integr., Vol. 23, 2690, 2015.

181. Shih, C.-C., Optics Communications, Vol. 118, 495, 1995.

182. Ozaktas, H. M., et al., IEEE Transactions on Signal Processing, Vol. 44, 2141, 1996.

183. Pei, S.-C., et al., Optics Letters, Vol. 22, 1047, 1997.

184. Liu, Y. N., et al., Pattern Anal. Appl., Vol. 19, 369, 2016.

185. Erguzel, T. T., et al., Comput. Biol. Med., Vol. 64, 127, 2015.

186. Maldonado, S., et al., Intell. Data Anal., Vol. 19, 1259, 2015.

187. Mudali, D., et al., Comput. Math. Method Med., 136921, 2015.

188. Shi, J. H., et al., J. Stat. Plan. Infer., Vol. 175, 87, 2016.

189. Washizawa, Y., IEICE Trans. Inf. Syst., Vol. E99D, 1353, 2016.

190. Thida, M., et al., IEEE T. Cybern., Vol. 43, 2147, 2013.

191. Yang, B., et al., Pattern Recognit., Vol. 55, 215, 2016.

192. Nguyen, V., et al., Appl. Comput. Rev., Vol. 15, 17, 2015.

193. Weinberger, K. Q., et al., Int. J. Comput. Vis., Vol. 70, 77, 2006.

194. Pulkkinen, S., Optim. Method Softw., Vol. 30, 1050, 2015.

195. Hong, C. Q., et al., Signal Process., Vol. 124, 132, 2016.

196. Jiang, J. F., et al., Teh. Vjesn., Vol. 23, 77, 2016.

197. Ueda, Y., et al., J. Signal Process. Syst. Signal Image Video Technol., Vol. 82, 151, 2016.

198. Yang, Y., et al., Mach. Learn., Vol. 74, 39, 2009.

199. Merentitis, A., et al., IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., Vol. 7, 1089, 2014.

200. Corander, J., et al., Stat. Comput., Vol. 23, 59, 2013.

201. Kothandan, R., Bioinformation, Vol. 11, 6, 2015.

202. Torgo, L., et al., Expert Syst., Vol. 32, 465, 2015.

203. Yin, Q. Y., et al., Math. Probl. Eng., 761814, 2013.

204. Siers, M. J., et al., Inf. Syst., Vol. 51, 62, 2015.

205. Mustafa, G., et al., 10th Iberian Conference on Information Systems and Technologies (CISTI),, 6, A. Rocha, et al. (eds.), IEEE, Agueda, Portugal, 2015.

206. Ren, Y. F., et al., Chin. J. Electron., Vol. 24, 52, 2015.

207. Smith, M. R., et al., International Joint Conference on Neural Networks,, 2690, IEEE, San Jose, CA, USA, 2011.

208. Sun, J. W., et al., Proceedings of Future Generation Communication and Networking,, 243, IEEE Computer Soc., Los Alamitos, 2007.

209. Saiyin, X. R. G., et al., Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics,, 704, S. B. Choi (ed.), Atlantis Press, Paris, 2015.

210. Kinaci, A. C., et al., 22nd International Conference on Neural Information Processing,, 440, S. Arik, et al. (eds.), Springer Int. Publishing Ag, Istanbul, Turkey, 2015.

211. Zhao, J. L., et al., Web Information Systems and Mining, 251, Z. Gong, et al. (eds.), Springer- Verlag Press, Berlin, 2011.

212. Prossegger, M., et al., Adaptive and Intelligent Systems, 182, A. Bouchachia (ed.), Springer Int. Publishing Ag, Cham, 2014.

213. Yang, B. S., et al., Advances in Knowledge Discovery and Data Mining, Proceedings, 405, T. Washio, et al. (eds.), Springer-Verlag Berlin, Berlin, 2008.

124. Jin, H. X., et al., IEEE International Conference on Automation and Logistics,, 359, IEEE, Electron Devices Soc. & Reliability Group, New York, 2007.

215. Zhu, W. T., et al., International Joint Conference on Neural Networks,, 800, IEEE, New York, 2014.

216. Singh, L., et al., Neural Information Processing, S. Arik, et al. (eds.), Pt. I, 302, Springer Int. Publishing Ag, Cham.

217. Alom, M. Z., et al., International Joint Conference on Neural Networks,, 1, IEEE, New York, 2015.

218. Singh, R. P., et al., 11th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP),, 393, J. Pan, et al. (eds.), Adelaide, Australia, 2015.

219. Barreto, G. A., et al., Neurocomputing, Vol. 176, 3, 2016.

220. Aldahoul, N., et al., Adv. Sci. Lett., Vol. 21, 3489, 2015.

221. Liu, T. C., et al., 18th International Conference on Intelligent Transportation Systems,, 1323, IEEE, Spain, 2015.

222. Lu, S., et al., Multimedia Tools and Applications, 2016, doi: 10.1007/s11042-016-3559-z.

223. Sun, J. Y., Neurocomputing, Vol. 79, 158, 2012.

224. Nurcahyo, S., et al., 2nd International Conference on Information and Communication Technology (ICOICT),, 166, IEEE, Bandung, Indonesia, 2014.

225. Dunea, D., et al., 1st International Work-Conference on Time Series (ITISE),, 804, I. R. Ruiz, et al. (eds.), Granada, Spain, 2014.

226. Chen, X. Y., et al., Water Resour. Manag., Vol. 30, 2179, 2016.

227. Karami, A., J. Netw. Comput. Appl., Vol. 56, 1, 2015.

228. Nienhold, D., et al., 3rd International Symposium on Computational And Business Intelligence,, 6, S. Deb, et al. (eds.), IEEE, Bali, Indonesia, 2015.

229. Jiao, L. M., et al., Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 461, S. Destercke, et al. (eds.), Springer-Verlag, Berlin, 2015.

230. Liu, C. M., et al., Advances in Knowledge Discovery and Data Mining, Part I, 176, T. Cao, et al. (eds.), Springer-Verlag Berlin, Berlin, 2015.

231. An, F. W., et al., Jpn. J. Appl. Phys., Vol. 55, 04ef10, 2016.

232. Dai, L. Z., et al., 4th National Conference on Electrical, Electronics and Computer Engineering,, 463, J. Wang, et al. (eds.), Atlantis Press, Paris, 2016.

233. Xie, Y., Hybrid Intelligent Systems, 13, A. Abraham, et al. (eds.), Springer-Verlag, Berlin, 2016.

234. Shi, J. Y., et al., Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques, 477, X. He, et al. (eds.), Springer Int. Publishing Ag, Cham, 2015.

235. Mihaljevic, B., et al., Advances in Artificial Intelligence, Caepia 2013, 159, C. Bielza, et al. (eds.), Springer-Verlag Berlin, Berlin, 2013.

236. Kwon, W. Y., et al., IEEE/RSJ International Conference on Intelligent Robots And Systems,, 3141, IEEE, Chicago, IL, 2014.

237. Krawczyk, B., et al., International Conference on Systems, Man and Cybernetics,, 2147, IEEE Computer Soc, Los Alamitos, 2015.

238. Benadit, P. J., et al., Proceedings of the International Conference on Information and Communication Technologies, ICICT 2014,, 184, P. Samuel (ed.), Elsevier Science Bv, Amsterdam, 2015.

239. Mohan, D., et al., 2nd International Conference on Communication Systems, K. Singh, et al. (eds.), Amer. Inst. Physics, Melville, 17, 2016.

240. Ling, P., et al., International Joint Conference on Neural Networks,, 438, IEEE, Beijing, Peoples R. China, 2014.

241. Lu, B. L., et al., Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015,, 31, Z. Liang, et al. (eds.), Atlantis Press, Paris, 2015.

242. Stylios, XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016.

243. Na, W., et al., 5th International Conference on Advanced Computational Intelligence,, 686, IEEE, Nanjing, Peoples R. China, 2012.

244. Xie, X. J., et al., International Joint Conference on Neural Networks,, 1, IEEE, Killarney, Ireland, 2015.

245. Dufrenois, F., et al., International Joint Conference on Neural Networks,, 12, IEEE, Killarney, Ireland, 2015.

246. Arumugam, P., et al., International Conferenced on Circuits, Power and Computing Technologies (ICCPCT),, 5, IEEE, Nagercoil, India, 2015.

247. Maeda, T., et al., International Conference on Image Processing,, 5177, IEEE, New York, 2014.

248. Wang, S., et al., Progress In Electromagnetics Research, Vol. 144, 171-184, 2014.

249. Kallas, M., et al., Pattern Recognit., Vol. 46, 3066, 2013.

250. Hejazi, M., et al., Digit. Signal Prog., Vol. 52, 72, 2016.

251. Mandavi, S., et al., Inf. Sci., Vol. 295, 407, 2015.

252. Mashwani, W. K., et al., Int. J. Adv. Comput. Sci. Appl., Vol. 7, 583, 2016.

253. Reina, D. G., et al., Int. J. Distrib. Sens. Netw., 2082496, 2016.

254. Jaiyeola, A. T., et al., Nat. Resour. Model., Vol. 28, 207, 2015.

255. Chong, C. K., et al., Curr. Bioinform., Vol. 9, 509, 2014.

256. Claveria, O., et al., East. Eur. Econ., Vol. 54, 171, 2016.

257. Zhang, Y., et al., Expert Syst., Vol. 33, 239, 2016.

258. Ji, G., et al., Entropy, Vol. 17, 5711, 2015.

259. Wang, S., et al., Simulation, Vol. 92, 637, 2016.

260. Pereyra, M., et al., IEEE J. Sel. Top. Signal Process., Vol. 10, 224, 2016.

261. Kovacevic, R. M., et al., Eur. J. Oper. Res., Vol. 237, 389, 2014.

262. Agapie, A., Business Excellence Challenges during the Economic Crisis, 1, C. Bratianu, et al. (eds.), Editura Univ. Transilvania Brasov, Romania, Brasov, 2012.

263. Suzuki, S., Eur. Phys. J.-Spec. Top., Vol. 224, 51, 2015.

264. Elsayed, S., et al., Int. J. Comput. Intell. Appl., Vol. 14, 1550025, 2015.

265. Afshar, A., et al., Water Resour. Manag., Vol. 29, 3891, 2015.

266. Ji, G., Math. Probl. Eng., 931256, 2015.

267. Akay, B., et al., Signal Image Video Process., Vol. 9, 967, 2015.

268. Kisi, O., et al., Appl. Math. Comput., Vol. 270, 731, 2015.

269. Patnaik, L. M., et al., Biomedical Signal Processing and Control, Vol. 1, 86, 2006.

270. El-Dahshan, E. S. A., et al., Digit. Signal Prog., Vol. 20, 433, 2010.

271. Dong, Z., et al., Expert Syst. Appl., Vol. 38, 10049, 2011.

272. Wu, L., Progress In Electromagnetics Research, Vol. 130, 369-388, 2012.

273. Saritha, M., et al., Pattern Recognit. Lett., Vol. 34, 2151, 2013.

274. Das, S., et al., Progress in Electromagnetics Research, Vol. 137, 1, 2013.

275. Dong, Z., et al., Entropy, Vol. 17, 1795, 2015.

276. Zhou, X., et al., Bioinformatics and Biomedical Engineering, 201, F.Ortuno, et al. (eds.), Springer International Publishing, Granada, Spain, 2015.

277. Feng, C., et al., Int. J. Imaging Syst. Technol., Vol. 25, 153, 2015.

278. Phillips, P., et al., Progress In Electromagnetics Research, Vol. 152, 41-58, 2015.

279. Sun, P., et al., Bio-medical Materials and Engineering, Vol. 26, 1283, 2015.

280. Dong, Z., et al., J. Med. Imaging Health Inform., Vol. 5, 1395, 2015.

281. Yang, X.-J., et al., Springer Plus, Vol. 4, 716, 2015.

282. Yang, X., et al., Entropy, Vol. 17, 8278, 2015.

283. Du, S., et al., Multimedia Tools and Applications, 2016, doi: 10.1007/s11042-016-3401-7.

284. Zhou, X.-X., et al., Simulation , 2016, doi: 10.1177/0037549716629227.

285. Lu, S., et al., Applied Sciences, Vol. 6, 169, 2016.

286. Wang, S., et al., Biomedical Engineering/Biomedizinische Technik, 2016, doi: 10.1515/bmt-2015-0152.

287. Sun, Y., et al., J. Med. Syst., Vol. 40, 173, 2016.

288. Johnson, D. M., et al., IEEE Trans. Knowl. Data Eng., Vol. 28, 1035, 2016.

289. Araujo, B., et al., Expert Syst. Appl., Vol. 45, 234, 2016.

290. Vong, W. K., et al., Psychon. Bull. Rev., Vol. 23, 230, 2016.

291. Bloom, V., et al., Comput. Vis. Image Underst., Vol. 144, 62, 2016.

292. Tang, J., et al., ACM Trans. Inf. Syst., Vol. 34, 7, 2016.

293. Shi, L. Y., et al., Sci. Rep., Vol. 6, 22804, 2016.

294. Chen, Y., et al., IEEE Trans. Image Process., Vol. 25, 988, 2016.

295. Wang, S., et al., Comput. Math. Method Med., Vol. 454076, 2015.

296. Zhang, Y., et al., Sensors, Vol. 11, 4721, 2011.

297. Alijla, B. O., et al., Inf. Sci., Vol. 325, 175, 2015.

298. Zhang, Y., Expert Syst. Appl., Vol. 36, 8849, 2009.

299. Sheta, A. F., et al., Int. J. Adv. Comput. Sci. Appl., Vol. 7, 499, 2016.

300. Klein, C. E., et al., IEEE Trans. Magn., Vol. 52, 7208304, 2016.


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