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Articles

Human Epithelial Type-2 Cell Image Classification Using an Artificial Neural Network with Hybrid Descriptors

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REFERENCES

  • Y. Kumar, A. Bhatia, and R. W. Minz, “Antinuclear antibodies and their detection methods in diagnosis of connective tissue diseases: A journey revisited,” Diagn. Pathol., Vol. 4, p. 1, Feb. 2009. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2628865/pdf/1746-1596-4-1.pdf doi: 10.1186/1746-1596-4-1
  • K. Subramaniam, M. P. Paulraj, and B. S. Divya. “Hep-2 cell classification using binary decision tree approach,” in 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), IEEE, Kuala Lumpur, 4–8 Dec. 2016, pp. 508–12.
  • C. Dahle, T. Skogh, A. K. Åberg, A. Jalal, and P. Olcén, “Methods of choice for diagnostic antinuclear antibody (ANA) screening: Benefit of adding antigen-specific assays to immunofluorescence microscopy”, J. Autoimmun., Vol. 22, no. 3, pp. 241–8, May 2004. doi: 10.1016/j.jaut.2003.12.004
  • P. Hobson, B. C. Lovell, G. Percannella, A. Saggese, M. Vento, and A. Wiliem, “Computer aided diagnosis for anti-nuclear antibodies HEp-2 images: Progress and challenges,” Pattern Recogn. Lett., Vol. 82, pp. 3–11, Oct. 2016. doi: 10.1016/j.patrec.2016.06.013
  • P. Foggia, G. Percannela, P. Soda, and M. Vento, “Benchmarking HEp-2 cells classification methods,” IEEE Trans. Med. Imag., Vol. 32, pp. 1878–89, Jun. 2013. doi: 10.1109/TMI.2013.2268163
  • P. Hobson, B. C. Lovell, G. Percannella, M. Ventoand, and A. Wiliem, “Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset,” Artificial Intelligence in Medicine, Vol. 65, pp. 239–50, Aug. 2015. doi: 10.1016/j.artmed.2015.08.001
  • P. Foggia, G. Percannella, A. Saggeseand, and M. Vento, “Pattern recognition in stained HEp-2 cells: Where are we now?,” Pattern Recogn., Vol. 47, no. 7, pp. 2305–14, Jul. 2014. doi: 10.1016/j.patcog.2014.01.010
  • A. Wiliem, Y. Wong, and C. Sanderson, “Classification of human epithelial type 2 cell indirect immunofluoresence images via codebook based descriptors,” in Proceedings of IEEE Workshop on Applications of Computer Vision (WACV), Florida, 17–18 Jan. 2013, pp. 95–102.
  • S. Manivannan, W. Li, S. Akbar, R. Wang, J. Zhang, and S. J. McKenna, “Hep-2 cell classification using multi-resolution local patterns and ensemble svms,” in Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images (I3A), ICPR 2014, 24–28 Aug. 2014, pp. 37–40.
  • D. Kastaniotis, F. Fotopoulou, I. Theodorakopoulos, G. Economou, and S. Fotopoulos, “HEp-2 cell classification with vector of hierarchically aggregated residuals,” Pattern Recogn., 65, pp. 47–57, May 2017. doi: 10.1016/j.patcog.2016.12.013
  • I. Theodorakopoulos, D. Kastaniotis, G. Economou, and S. Fotopoulos “Hep-2 cells classification using morphological features and a bundle of local gradient descriptors,” in Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images (I3A), ICPR 2014, 24–28 Aug. 2014, pp. 33–6.
  • S. Ensafi, S. Lu, A. A. Kassim, and C. L. Tan, “Accurate HEp-2 cell classification based on sparse coding of superpixels,” Pattern Recogn. Lett., 82, pp. 64–71, Feb. 2016. doi: 10.1016/j.patrec.2016.02.007
  • S. Monajemi, S. Ensafi, S. Lu, A. A. Kassim, C. L. Tan, S. Sanei, and S.-H. Ong, “Classification of HEp-2 cells using distributed dictionary learning,” in Proceedings of 24th European Signal Processing Conference, IEEE, Hungary, 29 Aug.–2 Sep., pp. 1163–7.
  • X. Jia, L. Shen, X. Zhou, and S. Yu, “Deep convolutional neural network based HEp-2 cell classification,” in 23rd International Conference on Pattern Recognition (ICPR 2016), IEEE, Mexico, 4–8 Dec. 2016, pp. 77–80.
  • G. Kumar and P. K. Bhatia, “A detailed review of feature extraction in image processing systems”, in 2014 Fourth International Conference Advanced Computing and Communication Technologies, IEEE India, 8–9 Feb., pp. 5–12.
  • B. S Divya, K. Subramaniam, and H. R. Nanjundaswamy, “Human epithelial type-2 cell categorization using hybrid descriptor with binary tree”, J. Ambient Intell. Humaniz. Comput., pp. 1–8, Feb. 2018. https://doi.org/10.1007/s12652-018-0694-6DOI:10.1007/s12652-018-0694-6
  • M. P. Paulraj, K. Subramaniam, S. B. Yaccob, A. Hamid, B. Adom, and C. R. Hema, “A Machine learning approach for distinguishing hearing perception level using auditory evoked potentials,” in 2014 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Malaysia, 8–10 Dec., pp. 991–6.
  • B. S. Divya, K. Subramaniam, and H. R. Nanjundaswamy, “HEp-2 cell classification using artificial neural network approach,” in 23rd International Conference on Pattern Recognition (ICPR 2016), IEEE, Mexico, 4–8 Dec. 2016, pp. 84–9.
  • J. Heaton, Introduction to Neural Networks for Java, 2nd ed. St. Louis: Heatson Research Inc., 2008.
  • M. F. Meiller, “A scaled conjugate gradient algorithm for fast supervised learning,” Neural Networks, Vol. 6, pp. 525–533, 1993. doi: 10.1016/S0893-6080(05)80056-5
  • Available: http://hep2.unisa.it (Available for ICPR 2016 Contestants only).
  • P. Hobson, B. C. Lovell, G. Percannella, A. Saggese, M. Vento, and Arnold Wiliem, “ HEp-2 staining pattern recognition at cell and specimen levels: Datasets, algorithms and results,” Pattern Recogn. Lett., Vol. 82, pp. 12–22, 15 Oct. 2016. doi: 10.1016/j.patrec.2016.07.013

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