Investigation of different neural models for blood cell type identification

Neural Computing and Applications

Volumn 21, Issue 6, 2012, Pages 1177-1183

  Khashman, Adnan ^a  

^a NEAR EAST UNIVERSITY   (Turkey)

Author keywords

Back propagation; Blood cell identification; Image processing; Neural networks; Rotational invariance

Indexed keywords

AUTOMATED METHODS; BACKPROPAGATION LEARNING ALGORITHM; BLOOD CELLS; CELL IDENTIFICATION; DATA FEATURE; INFORMATION CONCERNING; IRREGULAR SHAPE; MANUAL CLASSIFICATION; MICROSCOPE IMAGES; MICROSCOPIC IMAGE; MORPHOLOGICAL FEATURES; NEURAL MODELS; NEURAL NETWORK MODEL; ROTATIONAL INVARIANCES;

BACKPROPAGATION; CELLS; IMAGE PROCESSING; LEARNING ALGORITHMS; NEURAL NETWORKS;

BLOOD;

EID: 84865629724     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-010-0476-3     Document Type: Article

References (18)

1. Michalewicz Z, Schmidt M, Michalewicz M, Chiriac C (2005) Case study: an i{dotless}ntelligent decision-support system. IEEE Intell Syst 20(4): 44-49.
2. Sheikh H, Zhu B, Micheli-Tzanakou E (1997) Neural networks and blood cell identification. J Med Syst 21(4): 201-210.
3. Lin W, Xiao J, Micheli-Tzanakou E (1998) A computational intelligence system for cell classification. IEEE international conference on information technology applications to biomedicine, May 1998, pp 105-109.
4. Shitong W, Min W (2006) A new detection algorithm (NDA) based on fuzzy cellular neural networks for white blood cell detection. IEEE Trans Inf Technol Biomed 10(1): 5-10.
5. Buxton BF, Abdallahi H, Fernandez-Reyes D, Jarra W (2007) Development of an extension of the Otsu algorithm for multidimensional image segmentation of thin-film blood slides. IEEE i{dotless}nternational conference on computing: theory and applications, March 2007, pp 552-562.
6. Scotti F (2006) Robust segmentation and measurements techniques of white cells in blood microscope images. IEEE i{dotless}nstrumentation and measurement technology conference, April 2006, pp 43-48.
7. Wang W, Song H, Zhao Q (2006) A modified watersheds image segmentation algorithm for blood cell. IEEE i{dotless}nternational conference on communications, circuits and systems, vol 1, June 2006, pp 450-454.
8. Yi F, Chongxun Z, Chen P, Li L (2005) White blood cell image segmentation using on-line trained neural network. IEEE conference on engineering in medicine and biology, Sept 2005, pp 6476-6479.
9. Theera-Umpon N, Dhompongsa S (2007) Morphological granulometric features of nucleus in automatic bone marrow white blood cell classification. IEEE Trans Inf Technol Biomed 11(3): 353-359.
10. Mircic S, Jorgovanovic N (2006) Application of neural network for automatic classification of leukocytes. IEEE 8th seminar on neural network applications in electrical engineering, pp 141-144.
11. Markiewicz T, Osowski S, Marianska B, Moszczynski L (2005) Automatic recognition of the blood cells of myelogenous leukemia using SVM. Proceedings of i{dotless}nternational joint conference on neural networks, Canada, Aug 2005, pp 2496-2501.
12. Zheng Q, Milthorpe BK, Jones AS (2004) Direct neural network application for automated cell recognition. Cytometry A 57(1): 1-9.
13. Khashman A (2008) IBCIS: i{dotless}ntelligent blood cell identification system. Prog Nat Sci 18(10): 1309-1314.
14. Khashman A (2008) Blood cell identification using a simple neural network. Int J Neural Syst 18(5): 453-458.
15. Khashman A (2009) Application of an emotional neural network to facial recognition. Neural Comput Appl 18(4): 309-320.
16. Khashman A (2009) Blood cell identification using emotional neural networks. J Inf Sci Eng 25(6): 1737-1751.
17. Khashman A (2010) Blood cell type identification using different emotional neural network models. J Multiple Valued Logic Soft Comput 16(1-2): 17-35.
18. Khashman A (2010) An emotional system with application to blood cell type identification. Transactions of the i{dotless}nstitute of measurement and control, SAGE publications, pp 1-23. doi: 10. 1177/0142331210366640.