Barnes M R.

De Montfort University, Leiscester, England, United Kingdom

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Publications (6)5 Total impact

  • R.I John, P.R Innocent, M.R Barnes
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    ABSTRACT: This paper presents the results of using type 2 fuzzy sets to assist in the pre-processing of data for use with neuro-fuzzy clustering for classification of sports injuries in the lower leg. This research is concerned with the analysis of bone scans from stress related injuries to the tibia. Of particular interest is whether neural network based clustering techniques can help the consultant in classifying the images. The work was motivated by the situation where there is a relatively small amount of relevant data and difficulties are faced by consultants in classifying the various types of injuries. For this particular problem the consultant's interpretation of the image lends itself to representation using type 2 fuzzy sets. This research sets out to address whether, with fuzzy neuro-clustering techniques some insights may be provided to the consultant that they can use along with their experience and knowledge. The results of this approach indicate that the use of neural clustering using a type 2 representation can improve the classification of shin images.
    Information Sciences 06/2000; · 3.64 Impact Factor
  • Peter Innocent, Robert John, Barnes M R.
    01/2000: pages 361-393;
  • R.I. John, P.R. Innocent, M.R. Barnes
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    ABSTRACT: This paper is concerned with pre-processing of data for submission to neural networks. In particular the use of type 2 fuzzy sets to assist in this process is discussed and the results of using type 2 sets with FuzzyART is presented for clustering of radiographic tibia images. These results indicate that the approach out performs a type 1 approach, for certain tibia problems, and that the type 2 solution assisted the expert in analysing a set of images that he was unable to classify originally
    Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on; 06/1998
  • P R Innocent, M Barnes, R John
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    ABSTRACT: This paper concerns the classification analysis of exercise-induced lower leg pain by applying competitive neural network clustering and mapping techniques to type 1 and type 2 fuzzy descriptions of bone scan images of the tibia. The clusters are described and compared with each other and with the experts known classes that would be expected from medical findings. The discovered clusters provide training sets for supervised learning by an ARTMAP and similar neural network. These were used to classify the previously unclassified images and hence improve the classification process. The overall conclusion is that the use of the neural clustering methods has improved the classification process of the shin images despite the paucity of data and its inherent uncertainty.
    Artificial Intelligence in Medicine 12/1997; 11(3):241-63. · 1.36 Impact Factor
  • Proceedings of 2nd International Conference on Neural Networks and Expert Systems in Medicine and Healthcare (NNESMED 96); 08/1996
  • R I John, P R Innocent, M R Barnes

Publication Stats

87 Citations
5.00 Total Impact Points

Institutions

  • 1998–2000
    • De Montfort University
      • Centre for Computational Intelligence
      Leiscester, England, United Kingdom