Artificial neural network analysis of malaria severity through aggregation and deformability parameters of erythrocytes

Clinical Hemorheology and Microcirculation

Volumn 29, Issue 3-4, 2003, Pages 457-468

  Jayavanth, Sanjay ^a   Singh, Megha ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

Artificial neural network; Deformability; Erythrocyte aggregation; Malaria; Severity classification

Indexed keywords

RECOMBINANT ERYTHROPOIETIN;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BLOOD SAMPLING; CELL COUNT; COMPUTER PROGRAM; CONFERENCE PAPER; DISEASE SEVERITY; ERYTHROCYTE AGGREGATION; ERYTHROCYTE DEFORMABILITY; FILTRATION; FLOW MEASUREMENT; HUMAN; HUMAN CELL; LASER; MALARIA; MICROSCOPY; SEDIMENTATION RATE;

EQUIPMENT DESIGN; ERYTHROCYTE AGGREGATION; ERYTHROCYTE DEFORMABILITY; HEMORHEOLOGY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LASERS; MALARIA; MICROSCOPY, VIDEO; NEURAL NETWORKS (COMPUTER); PARASITEMIA; SEVERITY OF ILLNESS INDEX;

EID: 1642562610     PISSN: 13860291     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (34)

1. M. Aikawa, Morphological changes in erythrocytes induced by malaria parasites, Biol. Cell 84 (1988), 173-181.
2. D. Melville, F. Paul and S. Routh, Direct magnetic separation of the red cells from the whole blood, IEEE Trans. Magnetics 11 (1975), 1701-1704.
3. R.J. Howard and A.D. Gilladoga, Molecular study related to pathogenesis of cerebral malaria, Blood 74 (1989), 2603.
4. H.S. Ginsburg, S. Kuntner, M. Krugliak and I. Cabanchik, Characterization of permeation pathways appearing in host membrane of plasmodium falciparum infected red blood cell. Mol. Biochem. Paracitol. 14 (1985), 313-322.
5. I.W. Sherman and R.T. Greenan, Plasmodium falciparum: regional differences in the lectin and cationized ferritin binding to the surface of the malarial-infected human erythrocyte, Parasitol. 93 (1986), 17-32.
6. G.B. Nash, E. O'Brien, E.C. Gordon-Smith and J.A. Dormandy, Abnormalities in the mechanical properties of the red blood cell caused by plasmodium falciparum, Blood 74 (1989), 855-861.
7. H.M. Davis, T.M.E. Silamut, K. Bunnag, D. and N.J. White, Rosetting formation of P. falciparum from patients with acute malaria, Infection and Immunity 59 (1991), 2135-2139.
8. J.F. Stoltz, M. Singh and P. Riha, Hemorheology in Practice, IOS Press, Amsterdam, 1999.
9. M.R. Hardeman, P.T. Goedhart, J.G.G. Dobbe and K.P. Lettings, Laser-assisted optical rotational cell analyzer: A new instrument for measurement of various structural hemorheological parameters, Clin. Hemorheol. 14 (1994), 605-618.
10. M. Singh and M. Kumaravel, A computerized system for sequential analysis of aggregation of erythrocyte under dynamic conditions, Comput. Biomed. Res. 27 (1994), 325-336.
11. E. Karabetsos, Papaodysseus and D. Kautsouris, A new method for measuring red blood cell aggregation using pattern recognition techniques on backscattered ultrasound Doppler signals, Clin. Hemorheol. Microcirc. 20 (1999), 63-75.
12. E. Alexandratou, D. Yova and D. V. Cokkinos, Morphometric characteristics of red cells as diagnostic factors for coronary artery disease, Clin. Hemorheol. Microcirc. 21 (1999), 383-388.
13. P. Foresto, M. D'Arrigo, L. Racca et al., Comparative analysis of aggregate shapes by digital microscopic images. Application to hypertension, Clin. Hemorheol. Microcirc. 26 (2002), 137-144.
14. B. Yegnanarayana, Artificial Neural Networks, Prentice-Hall, New Delhi, India, 2001.
15. I. Dipti, B.S. Peter, J.A. Robert and J.O. William, Artificial neural networks: current status in cardiovascular medicine, J. Am. Cardiol. 28 (1996), 515-521.
16. Z. Zhou, Y. Jiang, Y. Yang and S. Chen, Lung cancer cell identification based on artificial neural network ensembles, Artificial Intell. Med. 24 (2002), 25-36.
17. R.T. Ritchings, M. McGillion and C.J. Moore, Pathological voice quality assessment using artificial neural networks, Med. Eng. Phys. 24 (2002), 561-564.
18. P.S.G. Lisboa, A review of evidence of health benefit from artificial neural networks in medical intervention, Neural Networks 15 (2002), 11-39.
19. I. Kononenko, Machine learning for medical diagnosis: history, state of art and perspective, Artif. Intell. Med. 23 (2001), 89-109.
20. R.C. Gonzalez and R.E. Wood, Digital Image Processing, Addison-Wesley, New York, USA, 1999.
21. L.B. Lucy, An iterative technique for the rectification of observed images, Astronom. J. 79 (1974), 8-37.
22. P. Soille, Morphological Image Analysis: Principles and Applications, Vol. I, Springer-Verlag, Berlin, Germany, 1999.
23. R.M. Haralick and L.G. Shapiro, Computer and Robot Vision, Addison-Wesley, New York, USA, 1992.
24. J. Sanjay, M. Singh and K. Jagadeesan, Sequential imaging and analysis of erythrocyte aggregation, in: Proc. 2nd Ann. Internat. IEEE-EMBS Conference on Micro-Technol. Med. and Biol., 2002, pp. 458-461.
25. M. Singh and M. Kumaravel, Sequential analysis of the influence of inhomogeneous magnetic field on aggregation mechanism of erythrocytes in health and malaria, Cell. Eng. 1 (1995), 21-27.
26. J. Sanjay and M. Singh, Changes in erythrocyte aggregation and deformability during human ageing, Curr. Sci. 82 (2002), 191-196.
27. P. Kumar and M. Clark, Clinical Medicine, WB Saunders, London, UK, 1999.
28. G.B. Nash, M.B. Cook, K. Marsh et al., Rheological analysis of the adhesive interactions of red blood cells parasitized by plasmodium falciparum, Blood 79 (1992), 798-807.
29. P. Patterson and S. Draper, A neural net representation of experienced and nonexperienced users during manual wheelchair propulsion, J. Rehab. Res. Dev. 3 (1998), 43-51.
30. M. Frize, C.M. Ennett, M. Stevenson and H.C.E. Trigg, Clinical decision support systems for intensive care units: using artificial neural networks, Med. Eng. Phys. 23 (2001), 217-225.
31. C. Chandrasekhar, Neural Network Models for Recognition of Stop Consonant-vowel Segments in Continuous Speech, Ph.D. Dissertation, Indian Institute of Technology, Madras, 1996.
32. L. Holmstrom and P. Koistnen, Using additive noise in back propagation training, IEEE Trans. Neural Networks 3 (1993), 24-38.
33. F.K. Glenister, R.L. Coppel, A.F. Cowman et al., Contribution of parasite proteins to altered mechanical properties of malaria-infected red blood cells, Blood 99 (2002), 1060-1063.
34. R. Puniyani, Clinical Hemorheology: New Horizons, New Age International, New Delhi, India, 1996.