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Artificial Neural Networks in Medical Diagnosis

  • Y. Fukuoka
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 96)

Abstract

The purpose of this chapter is to cover a broad range of topics relevant to artificial neural network techniques for biomedicine. The chapter consists of two parts: theoretical foundations of artificial neural networks and their applications to biomedicine. The first part deals with theoretical bases for understanding neural network models. The second part can be further divided into two subparts: the first half provides a general survey of applications of neural networks to biomedicine and the other half describes some examples from the first half in more detail.

Keywords

Acute Myeloid Leukemia Artificial Neural Network Acute Lymphoblastic Leukemia Medical Diagnosis Hide Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Haykin, S. (1994), Neural Networks: a Comprehensive Foundation, Macmillan College Publishing Company, New York.Google Scholar
  2. [2]
    Bishop, C.M. (1995), Neural Networks for Pattern Recognition, Oxford University Press, New York.Google Scholar
  3. [3]
    Anderson, L. (1995), Introduction to Neural Networks, MIT Press, Cambridge.Google Scholar
  4. [4]
    Hudson, D.L. and Cohen, M.E. (2000), Neural Networks and Artificial Intelligence for Biomedical Engineering, IEEE Press, Piscataway.Google Scholar
  5. [5]
    Lisboa, P.J.G., Ifeachor, E.C. and Szczepaniak, P. (Eds.) (1999), Artificial Neural Networks in Biomedicine (Perspectives in Neural Computing), Springer, London, Berlin, Heidelberg, New York.Google Scholar
  6. [6]
    Penny, W. and Frost, D. (1996), “Neural networks in clinical medicine,” Medical Decision Making, vol. 16, pp. 386–398.PubMedCrossRefGoogle Scholar
  7. [7]
    Marmarelis, V.Z. (Ed.) (1994), Advanced Methods of Physiological System Modeling Vol. III, Plenum Press, New York.Google Scholar
  8. [8]
    Miller, A.S., Blott, B.H. and Hames, T.K. (1992), “Review of neural network applications in medical imaging and signal processing,” Medical & Biological Engineering & Computing, vol. 30, pp. 449464.Google Scholar
  9. [9]
    McCullogh, W.W. and Pitts, W. (1941), “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biophysics, vol. 5, pp. 115–133.Google Scholar
  10. [10]
    Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986), “Learning representations by back-propagating errors,” Nature, vol. 323, pp. 533–536.CrossRefGoogle Scholar
  11. [I1]
    Rumelhart, D.E., McClelland, J.L. and the PDP Research Group (1986), Parallel Distribute Processing, MIT Press, Cambridge.Google Scholar
  12. [12]
    Hush, D.R., Horne, B., and Salas, J.M. (1992), “Error surfaces for multilayer perceptrons,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, pp. 1152–1161.CrossRefGoogle Scholar
  13. [13]
    Fukuoka, Y., Matsuki, H., Ishida, A. and Minamitani, H. (1998), “A modified back-propagation method to avoid false local minima,” Neural Networks, vol. 11, pp. 1059–1072.PubMedCrossRefGoogle Scholar
  14. [14]
    Xu, L., Klasa, S. and Yuille, A. (1992), “Recent advances on techniques of static feedforward networks with supervised learning,” Int. J. Neural Syst., vol. 3, pp. 253–290.CrossRefGoogle Scholar
  15. [15]
    Fukuoka, Y., Noshiro, M., Shindo, H., Minamitani, H. and Ishikawa, M. (1997), “Nonlinearity identified by neural network models in Pco2 system in humans,” Med. & Biol. Comput. & Eng., vol. 35, pp. 33–39.CrossRefGoogle Scholar
  16. [16]
    Kohonen, T. (1989), Self-organization and associative memory, Springer-Verlag, New York, Berlin, Heidelberg.Google Scholar
  17. [17]
    Kohonen, T. (1990), “Self-organizazing map,” Proc. of the IEEE, vol. 78, pp. 1464–1480.CrossRefGoogle Scholar
  18. [18]
    Rogers, S.K, Ruck, D.W. and Kabrisky, M. (1994), “Artificial neural networks for early detection and diagnosis of cancer,” Cancer Lett., vol. 77, pp. 79–83.PubMedCrossRefGoogle Scholar
  19. [19]
    Cybenko, G. (1989), “Approximation by superpositions of a sigmoid function,” Mathematics of Control, Signals, and Systems, vol. 2, pp. 303–314.Google Scholar
  20. [20]
    Funahashi, K. (1989), “On the approximate realization of continuous mapping by neural networks,” Neural Networks, vol. 2, pp. 183–192.CrossRefGoogle Scholar
  21. [21]
    Patil, S., Henry, J.W., Rubenfire, M. and Stein, P.D. (1993), “Neural network in the clinical diagnosis of acute pulmonary embolism,” Chest, vol. 104, pp. 1685–1689.PubMedCrossRefGoogle Scholar
  22. [22]
    Wilding, P., Morgan, M.A., Grygotis, A.E., Shoffner, M.A. and Rosato, E.F. (1994), “Application of backpropagation neural networks to diagnosis of breast and ovarian cancer,” Cancer Lett., vol. 77, pp. 145–153.PubMedCrossRefGoogle Scholar
  23. [23]
    Baxt, W.G. (1991), “Use of an artificial neural network for the diagnosis of myocardial infarction,” Ann. Intern. Med., vol. 115, pp. 843–848.PubMedCrossRefGoogle Scholar
  24. [24]
    Baxt, W.G. and White, H. (1995), “Bootstrapping confidence intervals for clinical input variable effects in a network trained to identify the presence of myocardial infarction,” Neural Computation, vol. 7 pp. 624–638.PubMedCrossRefGoogle Scholar
  25. [25]
    Holdaway, R.M., White, M.W. and Marmarou, A. (1990), “Classification of somatosensory-evoke potentials recorded from patients with severe head injuries,” IEEE Eng. in Med. & Biol. Mag., vol. 9, pp. 43–49.CrossRefGoogle Scholar
  26. [26]
    Hiraiwa, A., Shimohara, K. and Tokunaga, Y. (1990), “EEG topography recognition by neural networks,” IEEE Eng. in Med. & Biol. Mag., vol. 9, pp. 39–42.CrossRefGoogle Scholar
  27. [27]
    Jansen, B.H. (1990), “Artificial neural nets for K-complex detection,” IEEE Eng. in Med. & Biol. Mag., vol. 9, pp. 50–52.CrossRefGoogle Scholar
  28. [28]
    Ouyang, N., Ikeda, M. and Yamauchi, K. (1997), “Use of an artificial neural network to analyse an ECG with QS complex in V1_2 leads,” Med. & Biol. Eng. & Comput., vol. 35, pp. 556–560.CrossRefGoogle Scholar
  29. [29]
    Fukuoka, Y. and Ishida, A. (2000), “Chronic stress evaluation using neural networks,” IEEE Eng. in Med. & Biol. Mag., vol. 19, pp. 34–38.CrossRefGoogle Scholar
  30. [30]
    Kelly, M.F., PA Parker, P.A. and Scott, R.N. (1990), “The application of neural networks to myoelectric signal analysis: a preliminary study,” IEEE Trans. Biomed. Eng., vol. 37, pp. 221–230.PubMedCrossRefGoogle Scholar
  31. [31]
    Hopfield, J.J. and Tank, D.W. (1986), “Computing with neural circuits: A model,” Science, vol. 223, 625–633.CrossRefGoogle Scholar
  32. [32]
    Tank, D.W. and Hopfield, J.J. (1986), “Simple ‘neural’ optimization networks: An AID converter, signal decision circuitry, and a linear programming circuit,” IEEE Trans. Circuits Syst., vol. 33, pp. 533–541CrossRefGoogle Scholar
  33. [33]
    Schizas, C.N., Pattichis, C.S., Schofield, I.S., Fawcett, P.R. and Middleton, L.T. (1990), “Artificial neural nets in computer-aided macro motor unit potential classification,” IEEE Eng. in Med. & Biol. Mag., vol. 9, pp. 31–38.CrossRefGoogle Scholar
  34. [34]
    Cios, K.J., Chen, K. and Langenderfer, R.A. (1990), “Use of neural networks in detecting cardiac diseases from echocardiographic images,” IEEE Eng. in Med. & Biol. Mag., vol. 9, pp. 58–60.CrossRefGoogle Scholar
  35. [35]
    Buller, D., Buller, A., Innocent, P.R. and Pawlak, W. (1996), “Determining and classifying the region of interest in ultrasonic images of the breast using neural networks,” Artif Intell. Med., vol. 8, pp. 53–66.PubMedCrossRefGoogle Scholar
  36. [36]
    Chen, D.R., Chang, R.F. and Huang, Y.L. (1999), “Computer-aided diagnosis applied to US of solid breast nodules by using neural networks,” Radiology, vol. 213, pp. 407–412.PubMedGoogle Scholar
  37. [37]
    Frankel, D.S., Olson, R.J., Frankel, S.L. and Chisholm, S.W. (1989), “Use of a neural net computer system for analysis of flow cytometric data of phytoplankton populations,” Cytometry, vol. 10, pp. 540–550.PubMedCrossRefGoogle Scholar
  38. [38]
    Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L. and Sörnmo, L. (2000), “Clustering ECG complexes using Hermite functions and self-organizing maps,” IEEE Trans. Biomed. Eng., vol. 47, pp. 838–848.PubMedCrossRefGoogle Scholar
  39. [39]
    Benigni, R. and Pino, A. (1998), “Profiles of chemically-induced tumors in rodents: quantitative relationships,” Mutation Res. Fundamental & Molecular Mechanism Mutagenesis, vol. 421, pp. 93107.Google Scholar
  40. [40]
    Chen, D.R., Chang, R.F. and Huang, Y.L. (2000), “Breast cancer diagnosis using self-organizing map for sonography,” Ultrasound in Med. & Biol., vol. 26, pp. 405–411.CrossRefGoogle Scholar
  41. [41]
    Tamayo, P., Slonim, D., Mesirov, J., Zhu., Q., Kitareewan, S., Dmitrovsky, E. and Lander E.S., Gloub, T.R. (1999), “Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation,” Proc. Natl. Acad. Sci. USA, vol. 96, pp. 2907–2912.PubMedCrossRefGoogle Scholar
  42. [42]
    Gloub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, M., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D. and Lander E.S. (1999), “Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring,” Science, vol. 286, pp. 531–537.CrossRefGoogle Scholar
  43. [43]
    DeRisi, J., Penland, L., Brown, P.O., Bittner, M.L., Meltzer, P.S., Ray, M., Chen, Y., Su, Y.A. and Trent, J.M. (1996), “Use of a cDNA microarray to analyse gene expression patterns in human cancer,” Nature Genet., vol. 14, pp. 457–460.PubMedCrossRefGoogle Scholar
  44. [44]
    Wodicka, L., Dong, H., Mittmann, M., Ho, M.H. and Lockhart, D.J. (1997), “Genome-wide expression monitoring in Saccharomyces cerevisiae, ” Nature Biotechnol., vol. 15, pp. 1359–1367.CrossRefGoogle Scholar
  45. [45]
    Lockhart, D.J., Dong, H., Byrne, M.C., Follettie, M.T., Gallo, M.V., Chee, M.S., Mittmann, M., Wang, C., Kobayashi, M., Horton, H. and Brown, E.L. (1996), “Expression monitoring by hybridization to high-density oligonucleotide arrays,” Nature Biotechnol., vol. 14, pp. 1675–1680.CrossRefGoogle Scholar
  46. [46]
    Cottrell, G.W. and Munro, P. (1988), “Principal component analysis of image via back propagation,” SPIE, vol. 1001 Visual Communication and Image Processing ‘88, pp. 1070–1076.Google Scholar
  47. [47]
    Funahashi, K. (1990), “On the approximation realization of identity mappings by three-layer neural networks,” IEICE Trans., vol. J73-A, pp. 139–145. (in Japanese).Google Scholar
  48. [48]
    Iwata, A., Nagasaka, Y. and Suzumura, N. (1990), “Data compression of the ECG using neural network for digital Holter monitor,” IEEE Eng. in Med. & Biol. Mag., vol. 9, pp. 53–57.CrossRefGoogle Scholar
  49. [49]
    Chon, K.H. and Cohen, R.J. (1997), “Linear and nonlinear ARMA model parameter estimation using an artificial neural network,” IEEE Trans. Biomed. Eng., vol. 44, pp. 168–174.PubMedCrossRefGoogle Scholar
  50. [50]
    Prank, K., Jürgens, C., von zur Mühlen, A. and Brabant, G. (1998), “Predictive neural networks for learning the time course of blood glucose levels from the complex interaction of counterregulatory hormones,” Neural Computation, vol. 10, pp. 941–953.PubMedCrossRefGoogle Scholar
  51. [51]
    Robinson, P.R., Griffith, K., Gross, J.M. and O’Neill, M.C. (1999), “A back-propagation neural network predicts absorption maxima of chimeric human red/green visual pigments,” Vision Res., vol. 39, pp. 1707–1712.PubMedCrossRefGoogle Scholar
  52. [52]
    Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C., Lossos, I.S., Rosenwald, A., Boldrick, J.C., Sabet, H., Tran, T., Yu., X., Powell, J.I., Yang, L., Marti, G.E., Moore, T., Hudson, J. Jr., Lu, L., Lewis, D.B., Tibshirani, R., Sherlock, G., Chan, W.C., Greiner, T.C., Weisenburger, D.D., Armitage, J.O., Warnke, R., Levy, R., Wilson, W., Grever, M.R., Byrd, J.C., Botstein, D., Brown, P.O. and Staudt, L.M. (2000), “Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling,” Nature, vol. 403, pp. 503–511.PubMedCrossRefGoogle Scholar
  53. [53]
    Eisen, M.B., Spellman, P.T., Brown, P.O. and Bosteon, D. (1998), “Cluster analysis and display of genome-wide expression patterns,” Proc. Natl. Acad. Sci. USA, vol. 95, pp. 14863–14868.PubMedCrossRefGoogle Scholar
  54. [54]
    Pavlopoulos, S., Kyriacou E., Koutsouris, D., Blekas, K., Stafylopatis, A. and Zoumpoulis, P. (2000), “Fuzzy neural network-based texture analysis of ultrasonic images,” IEEE Eng. in Med. & Biol. Mag., vol. 19, pp. 39–47.CrossRefGoogle Scholar
  55. [55]
    Zahlmann, G., Scherf, M., Wegner, A., Obermainer, M. and Mertz, M. (2000), “Situation assessment of glaucoma using a hybrid fuzzy neural network,” IEEE Eng. in Med. & Biol. Mag., vol. 19, pp. 84–91.CrossRefGoogle Scholar
  56. [56]
    Dybowski, R., Weller, P., Chang, R. and Gant, V. (1996), “Prediction of outcome in critically ill patients using artificial neural network synthesized by genetic algorithm,” Lancet, vol. 347, pp. 11461150.Google Scholar
  57. [57]
    Stolorz, P., Lapedes, A. and Xia, Y. (1992), “Predicting protein secondary structure using neural net and statistical methods,” J. Mol. Biol., vol. 225, pp. 363–377.PubMedCrossRefGoogle Scholar
  58. [58]
    Ruggiero, C., Sacile, R. and Rauch, G. (1993), “Peptides secondary structure prediction with neural networks: a criterion for building appropriate learning sets,” IEEE Trans. Biomed. Eng., vol. 40, pp. 1114–1121.PubMedCrossRefGoogle Scholar
  59. [59]
    Farber, R. and Lapedes, A. (1992), “Determination of eukaryotic protein coding regions using neural networks and information theory,” J. Mol. Biol., vol. 226, pp. 471–479.PubMedCrossRefGoogle Scholar
  60. [60]
    Frishman, D. and Argos, P. (1992), “Recognition of distantly related protein sequences using conserved motifs and neural networks,” J. Mol. Biol., vol. 228, pp. 951–962.PubMedCrossRefGoogle Scholar
  61. [61]
    Mahadevan, I. and Ghosh, I. (1994), “Analysis of E.coli promoter structures using neural networks,” Nucl. Acids Res., vol. 22, pp. 2158–2165.PubMedCrossRefGoogle Scholar
  62. [62]
    Cloete, I. and Zurada, J.M. (Eds.) (2000), Knowledge-Based Neurocomputing, MIT Press, Cambridge.Google Scholar
  63. [63]
    Bigus, J.P. (1996), Data Mining with Neural Networks, McGraw-Hill, New York.Google Scholar

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© Springer-Verlag Berlin Heidelberg 2002

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  • Y. Fukuoka

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