SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE and FRACTAL DIMENSION for IDENTIFYING MULTIPLE SCLEROSIS

Fractals

Volumn 25, Issue 4, 2017, Pages

  Zhang, Yu Dong ^a,b   Zhang, Yin ^c   Phillips, Preetha ^d   Dong, Zhengchao ^e   Wang, Shuihua ^f,g  

^a NANJING NORMAL UNIVERSITY   (China)

^b Hunan Policy Academy   (China)

^c ZHONGNAN UNIVERSITY OF ECONOMICS AND LAW   (China)

^d WEST VIRGINIA SCHOOL OF OSTEOPATHIC MEDICINE   (United States)

^e NEW YORK STATE PSYCHIATRIC INSTITUTE   (United States)

^f CITY UNIVERSITY OF NEW YORK   (United States)

^g KYUSHU INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Biogeography Based Optimization; Box Counting; Canny Edge Detector; Fractal Dimension; Minkowski Bouligand Dimension; Synthetic Minority Oversampling Technique; Three Segment Representation

Indexed keywords

BRAIN MAPPING; ECOLOGY; HEURISTIC ALGORITHMS; NETWORK LAYERS;

BIOGEOGRAPHY-BASED OPTIMIZATIONS; BOX-COUNTING; BRAIN DISEASE; CANNY EDGE DETECTORS; MINKOWSKI; MINKOWSKI-BOULIGAND DIMENSION; MULTIPLE SCLEROSIS; STATISTICAL INDICES; SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUES; THREE-SEGMENT REPRESENTATION;

FRACTAL DIMENSION;

EID: 85020267843     PISSN: 0218348X     EISSN: 17936543     Source Type: Journal    
DOI: 10.1142/S0218348X17400102     Document Type: Article

References (69)

1. A. Moroso et al., Cerebellar assessment in early multiple sclerosis, Cerebellum 16(2) (2017) 607-6112.
2. Y. Karaca et al., Rank determination of mental functions by 1D wavelets and partial correlation, J. Med. Syst. 41(1) (2017) 2.
3. Y. Karaca, The differential diagnosis of multiple sclerosis using convex combination of infinite Kernels, CNS Neurol. Disord. Drug Targets 16(1) (2017) 36-43.
4. J. A. N. Bijvank et al., A rare cause for visual symptoms in multiple sclerosis: Posterior internuclear ophthalmoplegia of Lutz, a historical misnomer, J. Neurol. 264(3) (2017) 600-602.
5. N. Ghasemi, S. Razavi and E. Nikzad, Multiple sclerosis: Pathogenesis, symptoms, diagnoses and cell-based therapy, Cell J. 19(1) (2017) 1-10.
6. L. S. Lens-Pechakova, Centenarian rates and life expectancy related to the death rates of multiple sclerosis, asthma, and rheumatoid arthritis and the incidence of type 1 diabetes in children, Rejuvenation Res. 19(1) (2016) 53-58.
7. M. Ingrisch et al., Dynamic contrast-enhanced magnetic resonance imaging suggests normal perfusion in normal-appearing white matter in multiple sclerosis, Invest. Radiol. 52(3) (2017) 135-141.
8. M. Pardini et al., The association between retinal nerve fibre layer thickness and N-acetyl aspartate levels in multiple sclerosis brain normal-appearing white matter: A longitudinal study using magnetic resonance spectroscopy and optical coherence tomography, Eur. J. Neurol. 23(12) (2016) 1769-1774.
9. L. Wu, Classification of fruits using computer vision and a multiclass support vector machine, Sensors, 12(9) (2012) 12489-12505.
10. J. M. Sabater-Navarro et al., Computer vision haemorrhage detection, Br. J. Surg. 104 (2017) 24-24.
11. S. Wang and L. Wu, A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO, Prog. Electromagn. Res. 109 (2010) 325-343.
12. V. Murray, P. Rodriguez and M. S. Pat-tichis, Multiscale AM-FM demodulation and image reconstruction methods with improved accuracy, IEEE Trans. Image Process. 19(5) (2010) 1138-1152.
13. D. R. Nayak, R. Dash and B. Majhi, Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests, Neurocomputing 177 (2016) 188-197.
14. T. M. Zhan and Y. Chen, Multiple sclerosis detection based on biorthogonal wavelet transform, RBF Kernel principal component analysis, and logistic regression, IEEE Access 4 (2016) 7567-7576.
15. J. Yang, Pathological brain detection in MRI scanning via Hu moment invariants and machine learning, J. Exp. Theor. Artif. Intell. 29(2) (2017) 299-312.
16. X.-X. Zhou, Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: Decision tree, k-nearest neighbors, and support vector machine, Simulation 92(9) (2016) 861-871.
17. M. F. Siddiqui, A. W. Reza and J. Kanesan, An automated and intelligent medical decision support system for brain MRI scans classification, PLoS One 10(8) (2015) e0135875.
18. P. Phillips, Z. Dong and J. Yang, Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization, Prog. Electromagn. Res. 152 (2015) 41-58.
19. H. J. Fu et al., Characteristics of pore structure and fractal dimension of low-rank coal: A case study of lower Jurassic Xishanyao coal in the southern Jung-gar basin, NW China, Fu e l 193 (2017) 254-264.
20. J. S. Lee, K. J. Wei and K. R. Wen, Image structure rebuilding technique using fractal dimension on the best match patch searching, Multimed. Tools Appl. 76(2) (2017) 1875-1899.
21. S. K. Kumar, R. Ganguli and D. Harursampath, Detecting width-wise partial delamination in the composite beam using generalized fractal dimension, Smart Struct. Syst. 19(1) (2017) 91-103.
22. Z. Arabasadi et al., Computer-aided decision making for heart disease detection using hybrid neural network-genetic algorithm, Comput. Methods Programs Biomed. 141 (2017) 19-26.
23. G. Ji, An MR brain images classifier system via particle swarm optimization and kernel support vector machine, Sci. World J. 2013 (2013) 130134.
24. S. Bahrami and F. D. Ardejani, Prediction of pyrite oxidation in a coal washing waste pile using a hybrid method, coupling artificial neural networks and simulated annealing (ANN/SA), J. Clean. Prod. 137 (2016) 1129-1137.
25. G. Yang, Automated classification of brain images using wavelet-energy and biogeography-based opti-mization, Multimed. Tools Appl. 75(23) (2016) 15601-15617.
26. Y. H. Lee, K. W. Kim and S. S. Kim, CUDA implementation of histogram stretching function for improving X-ray image, in 14th World Congress on Medical and Health Informatics. Ios Press, Copenhagen, Denmark; 2013, pp. 987-987.
27. X. Wu, Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization, Simulation 92(9) (2016) 873-885.
28. G. Wei, Color image enhancement based on HVS and PCNN, Science China Inform. Sci. 53(10) (2010) 1963-1976.
29. A. Gandhamal et al., Local gray level S-curve transformation-A generalized contrast enhancement technique for medical images, Comput. Biol. Med. 83 (2017) 120-133.
30. K. G. Lore, A. Akintayo and S. Sarkar, LLNet: A deep autoencoder approach to natural low-light image enhancement, Pattern Recognit. 61 (2017) 650-662.
31. M. Zieba, J. M. Tomczak and A. Gonczarek, RBM-SMOTE: Restricted Boltzmann machines for synthetic minority oversampling technique, in 7th Asian Conference on Intelligent Information and Database Systems (ACIIDS). (Springer, 2015, Bali, Indonesia), pp. 377-386.
32. K. B. Pawar and S. L. Nalbalwar, Distributed Canny edge detection algorithm using morphological filter, in International Conference on Recent Trends In Electronics, Information & Communication Technology (IEEE, Bengaluru, India, 2016), pp. 1523-1527.
33. Y. Jang, J. Mun and J. Kim, Resource-efficient FPGA architecture of canny edge detector, in Inter-national Society Design Conference (IEEE, Jeju, South Korea, 2016), pp. 299-300.
34. S. Liu et al., A fractal image encoding method based on statistical loss used in agricultural image compression, Multimed. Tools Appl. 75(23) (2016) 15525-15536.
35. S. Liu, Distribution of primary additional errors in fractal encoding method, Multimed. Tools Appl. 76(4) (2017) 5787-5802.
36. J. J. Martin-Sotoca et al., New segmentation method based on fractal properties using singularity maps, Geoderma 287 (2017) 40-53.
37. J. B. Florindo, O. M. Bruno and G. Landini, Morphological classification of odontogenic kerato-cysts using Bouligand-Minkowski fractal descriptors, Comput. Biol. Med. 81 (2017) 1-10.
38. M. Liu et al., Distributional escape time algorithm based on generalized fractal sets in cloud environment, Chinese J. Electron. 24(1) (2015) 124-127.
39. C. Cattani and A. Ciancio, On the fractal distribution of primes and prime-indexed primes by the binary image analysis, Physica A 460 (2016) 222-229.
40. C. Cattani and G. Pierro, On the fractal geometry of DNA by the binary image analysis, Bull. Math. Biol. 75(9) (2013) 1544-1570.
41. E. W. Dekking and F. M. Dekking, Helge von Koch's snowflake curve revisited, Am. Math. Mon. 123(2) (2016) 181-184.
42. X.-Q. Chen, Fractal dimension estimation for developing pathological brain detection system based on Minkowski-Bouligand method, IEEE Access 4 (2016) 5937-5947.
43. X. J. Yang et al., On a fractal LC-electric circuit modeled by local fractional calculus, Commun. Non-linear Sci. Numer. Simul. 47 (2017) 200-206.
44. X. J. Yang, F. Gao and H. M. Srivastava, Exact travelling wave solutions for the local fractional two-dimensional Burgers-type equations, Comput. Math. Appl. 73(2) (2017) 203-210.
45. J. A. T. Machado, D. Baleanu and C. Cattani, On exact traveling-wave solutions for local fractional Korteweg-de Vries equation, Chaos 26(8) (2016) 084312.
46. S. Lu and X. Qiu, A pathological brain detection system based on extreme learning machine optimized by bat algorithm, CNS Neurol. Disord. Drug Targets 16(1) (2017) 23-29.
47. T. Yuksel, Intelligent visual servoing with extreme learning machine and fuzzy logic, Expert Syst. Appl. 72 (2017) 344-356.
48. S. Du, Alzheimer's disease detection by pseudo zernike moment and linear regression classification, CNS Neurol. Disord. Drug Targets 16(1) (2017) 11-15.
49. Y. Chen, A feature-free 30-disease pathological brain detection system by linear regression classifier, CNS Neurol. Disord. Drug Targets 16(1) (2017) 5-10.
50. A. Seal et al., UGC-JU face database and its benchmarking using linear regression classifier, Multimed. Tools Appl. 74(9) (2015) 2913-2937.
51. J. Yang, Identification of green, Oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine, Entropy 17(10) (2015) 6663-6682.
52. J. Yang, Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM), Entropy 17(4) (2015) 1795-1813.
53. A. Liu, Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine, J. Med. Imaging Health Inform. 5(7) (2015) 1395-1403.
54. Z. Dong, Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree, Prog. Electro-magn. Res. 144 (2014) 171-184.
55. J. Arias, J. A. Gamez and J. M. Puerta, Learning distributed discrete Bayesian network classifiers under mapreduce with Apache spark, Knowl.-Based Syst. 117 (2017) 16-26.
56. R. V. Rao, A. Liu and L. Wei, Abnormal breast detection in mammogram images by feed-forward neural network trained by jaya algorithm, Fundam. Inform. 151(1-4) (2017) 191-211.
57. J. Li, Texture analysis method based on fractional Fourier entropy and fitness-scaling adaptive genetic algorithm for detecting left-sided and right-sided sensorineural hearing loss, Fundam. Inform. 151(1-4) (2017) 505-521.
58. I. Aydogdu, Cost optimization of reinforced concrete cantilever retaining walls under seismic loading using a biogeography-based optimization algorithm with Levy flights, Eng. Optim. 49(3) (2017) 381-400.
59. L. Wei, Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization, Entropy 17(8) (2015) 5711-5728.
60. M. Khishe, M. R. Mosavi and M. Kaveh, Improved migration models of biogeography-based optimization for sonar dataset classification by using neural network, Appl. Acoust. 118 (2017) 15-29.
61. W. L. Lim et al., A biogeography-based optimization algorithm hybridized with tabu search for the quadratic assignment problem, Comput. Intell. Neu-rosci. (2016) 5803893.
62. Z. Hasan, K. Salman and M. E. El-Hawary, Linear quadratic regulator design for power system stabilizer using biogeography based optimization method, in IEEE Canadian Conference on Electrical And Computer Engineering (IEEE, New York, Vancouver, Canada, 2016).
63. B. Y. Bagde et al., Optimal network reconfiguration of a distribution system using biogeography based optimization, in 6th International Conference on Power Systems (ICPS) (IEEE, New Delhi, India, 2016), p. 6.
64. Y.-K. Huo and G. Wei, An adaptive threshold for the Canny operator of edge detection, in International Conference on Image Analysis and Signal Processing (IEEE, Xiamen, China, 2010), pp. 371-374.
65. I. Rossales and M. Luppe, Architecture for fractal dimension estimation based on minkowski-bouligand method using integer distances, in 27th International Conference on Application-Specific Systems, Architectures and Processors (IEEE, London, England, 2016), pp. 231-232.
66. S. Liu et al., Numeric characteristics of generalized M-set with its asymptote, Appl. Math. Comput. 243 (2014) 767-774.
67. S. Liu et al., Fractal property of generalized M-set with rational number exponent, Appl. Math. Com-put. 220 (2013) 668-675.
68. A. J. Asman et al., Multi-atlas learner fusion: An efficient segmentation approach for large-scale data, Med. Image Anal. 26(1) (2015) 82-91.
69. Y. Huo et al., Consistent cortical reconstruction and multi-atlas brain segmentation, Neuroimage 138 (2016) 197-210.