MS Identification in Brain Magnetic Resonance Images Using Wavelet Transfer Learning

Subject Areas : Journal of Computer & Robotics

Ali Alijamaat ¹ , Ali NikravanShalmani ² , Peyman Bayat ³

1 - Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
2 - Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
3 - Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran

Received: 2020-11-09 Accepted : 2021-01-19 Published : 2020-12-01

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References:

[1] D. S. Reich, C. F. Lucchinetti, and P. A. Calabresi, "Multiple Sclerosis," New England Journal of Medicine, vol. 378, no. 2, pp. 169-180, (2018).
[2] C. H. Polman et al., "Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria," (in eng), Annals of neurology, vol. 69, no. 2, pp. 292-302, (2011).
[3] N. N. Sommer et al., "Multiple Sclerosis: Improved Detection of Active Cerebral Lesions With 3-Dimensional T1 Black-Blood Magnetic Resonance Imaging Compared With Conventional 3-Dimensional T1 GRE Imaging," (in eng), Invest Radiol, vol. 53, no. 1, pp. 13-19, Jan (2018).
[4] R. Zivadinov, M. Zorzon, R. De Masi, D. Nasuelli, and G. Cazzato, "Effect of intravenous methylprednisolone on the number, size and confluence of plaques in relapsing–remitting multiple sclerosis," Journal of the Neurological Sciences, vol. 267, no. 1, pp. 28-35, (2008).
[5] S. Jain et al., "Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images," (in eng), Neuroimage Clin, vol. 8, pp. 367-75, (2015).
[6] J. D. Dworkin et al., "An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions," (in eng), AJNR Am J Neuroradiol, vol. 39, no. 4, pp. 626-633, Apr (2018).
[7] M. Havaei et al., "Brain tumor segmentation with Deep Neural Networks," (in eng), Med Image Anal, vol. 35, pp. 18-31, Jan (2017).
[8] Y.-D. Zhang, Y. Zhang, P. Phillips, Z. Dong, and S. Wang, "Synthetic Minority Oversampling Technique and Fractal Dimension for Identifying Multiple Sclerosis," Fractals, vol. 25, January 01, (2017).
[9] O. Ghribi, L. Sellami, M. Ben Slima, A. Ben Hamida, C. Mhiri, and K. B. Mahfoudh, "An Advanced MRI Multi-Modalities Segmentation Methodology Dedicated to Multiple Sclerosis Lesions Exploration and Differentiation( ",in eng), IEEE Trans Nanobioscience, vol. 16, no. 8, pp. 656-665, Dec (2017).
[10] W. Xueyan and L. Mason, "Multiple Sclerosis Slice Identification by Haar Wavelet Transform and Logistic Regression," in Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017), 2017: Atlantis Press.
[11] Y.-D. Zhang, C. Pan, J. Sun, and C. Tang, "Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU," Journal of Computational Science, vol. 28, pp. 1-10, 2018/09/01/(2018).
[12] S.-H. Wang et al., "Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling," (in eng), Frontiers in neuroscience, vol. 12, pp. 818-818,(2018).
[13] Z. Ullah, M. Farooq ,S.-H. Lee, and D. An, "A Hybrid Image Enhancement Based Brain MRI Images Classification Technique," Medical Hypotheses, vol. 143, p. 109922, 06/01 (2020).
[14] A. Rezaee, K. Rezaee, J. Haddadnia, and H. T. Gorji, "Supervised meta-heuristic extreme learning machine for multiple sclerosis detection based on multiple feature descriptors in MR images," SN Applied Sciences, vol. 2, no. 5, p. 866, 2020/04/09 (2020).
[15] F. Chollet, Deep Learning with Python. Manning Publications Co., (2017).
[16] K. Weiss and T. Khoshkoftaar, "A Study of the Impact of Base Traditional Learners on Transfer Learning Algorithms," International Journal on Artificial Intelligence Tools, vol. 27, 06/27 (2018).
[17] D. Sarkar, R. Bali, and T. Ghosh, Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras. Packt Publishing, (2018).
[18] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 2016, pp. 770-778.
[19] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," arXiv e-prints, p. arXiv:1512.00567, (2015).
[20] M. Tan and Q. V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," arXiv e-prints, p. arXiv:1905.11946, (2019).
[21] N. Boussion et al., "A multiresolution image based approach for correction of partial volume effects in emission tomography," Physics in Medicine and Biology, vol. 51, no. 7, pp. 1857-1876, (2006).
[22] S. Jingjing, Y. Ming, X. Bugao, and P. Bel, "Fabric wrinkle characterization and classification using modified wavelet coefficients and support-vector-machine classifiers," Textile Research Journal, vol. 8 2 ,
no. 9, pp. 902-913, 2011/06/01 (2011).
[23] M. Vishwanath, "The recursive pyramid algorithm for the discrete wavelet transform," IEEE Transactions on Signal Processing, vol. 42, no. 3, pp. 673-676, (1994).
[24] N. X. Ríos-Cota and Á. Bernal-Noreña, "Arquitectura hardware para la implementación de la transformada discreta Wavelet 2D," Ingeniería y competitividad, vol. 16, pp. 69-81, (2014).
[25] M. Abadi and e. al., "TensorFlow: A System for Large-Scale Machine Learning," presented at the 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), Savannah, GA, (2016).