Neuro-fuzzy clustering of radiographic tibia image data using type 2 fuzzy sets

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.