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Articles

Optimised Feature Selection for Identification of Carcinogenic Leukocytes Using Weighted Aggregation Based Transposition PSO

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REFERENCES

  • J. Poomcokrak, and C. Neatpisarnvanit, “Red blood cells extraction and counting,” in The 3rd International Symposium on Biomedical Engineering, 2008, pp. 199–203.
  • A. Biondi, G. Cimino, R. Pieters, and C.-H. Pui, “Biological and therapeutic aspects of infant leukemia,” Blood, Vol. 96, no. 1, pp. 24–33, 2000. doi: 10.1182/blood.V96.1.24
  • S. C. Neoh, W. Srisukkham, L. Zhang, S. Todryk, B. Greystoke, C. P. Lim, M. A. Hossain, and N. Aslam, “An intelligent decision support system for leukaemia diagnosis using microscopic blood images,” Sci. Rep., Vol. 5, 2015, pp. 14938. doi: 10.1038/srep14938
  • H. T. Madhloom, S. A. Kareem, and H. Ariffin, “A robust feature extraction and selection method for the recognition of lymphocytes versus acute lymphoblastic leukemia,” in Advanced Computer Science Applications and Technologies (ACSAT), 2012 International Conference on, IEEE, 2012, pp. 330–335.
  • S. Agaian, M. Madhukar, and A. T. Chronopoulos, “Automated screening system for acute myelogenous leukemia detection in blood microscopic images,” IEEE Syst. J., Vol. 8, no. 3, pp. 995–1004, 2014. doi: 10.1109/JSYST.2014.2308452
  • N. Sinha, and A. Ramakrishnan, “Automation of differential blood count,” in TENCON 2003. Conference on Convergent Technologies for the Asia- Pacific Region, Vol. 2, IEEE, 2003, pp. 547–551.
  • V. Singhal, and P. Singh, “Local binary pattern for automatic detection of acute lymphoblastic leukemia,” in Communications (NCC), 2014 Twentieth National Conference on, IEEE, 2014, pp. 1–5.
  • S. H. Rezatofighi, and H. Soltanian-Zadeh, “Automatic recognition of five types of white blood cells in peripheral blood,” Comput. Med. Imaging Graph., Vol. 35, no. 4, pp. 333–343, 2011. doi: 10.1016/j.compmedimag.2011.01.003
  • L. Putzu, G. Caocci, and C. Di Ruberto, “Leucocyte classification for leukaemia detection using image processing techniques,” Artif. Intell. Med., Vol. 62, no. 3, pp. 179–191, 2014. doi: 10.1016/j.artmed.2014.09.002
  • F. Scotti, “Robust segmentation and measurements techniques of white cells in blood microscope images,” in Instrumentation and Measurement Technology Conference, 2006. IMTC 2006. Proceedings of the IEEE, IEEE, 2006, pp. 43–48.
  • V. Piuri, and F. Scotti, “Morphological classification of blood leucocytes by microscope images,” in Computational Intelligence for Measurement Systems and Applications, 2004. CIMSA. 2004 IEEE International Conference on, IEEE, 2004, pp. 103–108.
  • S. Mohapatra, D. Patra, and S. Satpathy, “An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images,” Neural Comput. Appl., Vol. 24, no. 7–8, pp. 1887–1904, 2014. doi: 10.1007/s00521-013-1438-3
  • D.-C. Huang, and K.-D. Hung, “Leukocyte nucleus segmentation and recognition in colour blood-smear images,” in Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International, IEEE, 2012, pp. 171–176.
  • S. Osowski, R. Siroic, T. Markiewicz, and K. Siwek, “Application of support vector machine and genetic algorithm for improved blood cell recognition,” IEEE Trans. Instrum. Meas., Vol. 58, no. 7, pp. 2159–2168, 2009. doi: 10.1109/TIM.2008.2006726
  • H. J. Escalante, et al., “Acute leukemia classification by ensemble particle swarm model selection,” Artif. Intell. Med., Vol. 55, no. 3, pp. 163–175, 2012. doi: 10.1016/j.artmed.2012.03.005
  • W. Srisukkham, L. Zhang, S. C. Neoh, S. Todryk, and C. P. Lim, “Intelligent leukaemia diagnosis with bare-bones pso based feature optimization,” Appl. Soft. Comput., Vol. 56, pp. 405–419, 2017. doi: 10.1016/j.asoc.2017.03.024
  • A. Kalaivani, and S. Chitrakala, “An optimal multi-level backward feature subset selection for object recognition,” IETE. J. Res., Vol. 65, no. 4, 1–13, 2018.
  • S. P. Kumar, and M. V. Latte, “Lung parenchyma segmentation: fully automated and accurate approach for thoracic CT scan images,” IETE. J. Res., 1–14, 2018. DOI:10.1080/03772063.2018.1494519.
  • M. Lohith, and M. Eshwarappa, “Multimodal biometrics for person identification using ear and palm print features,” IETE. J. Res., 1–8, 2018. DOI:10.1080/03772063.2018.1531069.
  • R. C. Gonzalez, and R. E. Woods. Digital image processing. 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2002.
  • S. Kar, K. Das Sharma, and M. Maitra, “Adaptive weighted aggregation in group improvised harmony search for lung nodule classification,” J. Exp. Theor. Artif. Intell. DOI:10.1080/0952813X.2019.1647561.
  • S. Kar, K. D. Sharma, and M. Maitra, “Gene selection from microarray gene expression data for classification of cancer subgroups employing pso and adaptive k-nearest neighborhood technique,” Expert Syst. Appl., Vol. 42, no. 1, pp. 612–627, 2015. doi: 10.1016/j.eswa.2014.08.014
  • A. Jemal, R. Siegel, E. Ward, T. Murray, J. Xu, C. Smigal, and M. J. Thun, “Cancer statistics, 2006,” CA Cancer J. Clin., Vol. 56, no. 2, pp. 106–130, 2006. doi: 10.3322/canjclin.56.2.106
  • R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. man Cybern., Vol. SMC-3, no. 6, pp. 610–621, 1973. doi: 10.1109/TSMC.1973.4309314
  • R. Eberhart, and J. Kennedy, “A new optimizer using particle swarm theory,” in Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on. IEEE, 1995, pp. 39–43.
  • S. Wang, P. Phillips, J. Yang, P. Sun, and Y. Zhang, “Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients,” Biomed. Eng./Biomed. Tech., Vol. 61, no. 4, pp. 431–441, 2016. doi: 10.1515/bmt-2015-0152
  • Y. Zhang, et al., “Multivariate approach for Alzheimer’s disease detection using stationary wavelet entropy and predator-prey particle swarm optimization,” J. Alzheimer's Dis., Vol. 65, no. 3, pp. 855–869, 2018. doi: 10.3233/JAD-170069
  • H. Ishibuchi, Y. Setoguchi, H. Masuda, and Y. Nojima, “Performance of decomposition-based many-objective algorithms strongly depends on pareto front shapes,” IEEE Trans. Evol. Comput., Vol. 21, no. 2, pp. 169–190, 2017. doi: 10.1109/TEVC.2016.2587749
  • K. E. Parsopoulos, and M. N. Vrahatis, “Multi-objective particles swarm optimization approaches,” in Multi-objective Optimization in Computational Intelligence: Theory and Practice. IGI global; 2008, pp. 20–42. DOI:10.4018/978-1-59904-498-9.ch002.
  • R. D. Labati, V. Piuri, and F. Scotti, “All-idb: The acute lymphoblastic leukemia image database for image processing,” in Image processing (ICIP), 2011 18th IEEE international conference on, IEEE, 2011, pp. 2045–2048.
  • M. Vania, D. Mureja, and D. Lee, “Automatic spine segmentation using convolutional neural network via redundant generation of class labels for 3D spine modeling,” arXiv preprint arXiv:1712.01640 (2017).
  • S. Mohapatra, S. S. Samanta, D. Patra, and S. Satpathi, “Fuzzy based blood image segmentation for automated leukemia detection,” in Devices and Communications (ICDeCom), 2011 International Conference on. IEEE, 2011, pp. 1–5.
  • F. Sadeghian, Z. Seman, A. R. Ramli, B. H. A. Kahar, and M.-I. Saripan, “A framework for white blood cell segmentation in microscopic blood images using digital image processing,” Biol. Proced. Online, Vol. 11, no. 1, pp. 196, 2009. doi: 10.1007/s12575-009-9011-2
  • M. Madhukar, S. S. Agaian, and A. T. Chronopoulos, “New decision support tool for acute lymphoblastic leukemia classification,” in Image Processing: Algorithms and Systems/Parallel Processing for Imaging Applications, 829518, 2012.

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