Abstract
This chapter presents an application of plastic (adaptive) neural network models to aid in the medical pattern application task of image classification by an expert clinician. We present a detailed description of the context and use of these neural models and show that the plasticity of the model and fuzzy representation are of particular value in this application. The neural models are considered as a model of how an expert would classify images from fuzzy linguistic and numeric descriptions.
Our model of the clinician is that, given a new image to classify, a cluster of similar images is produced (recall of similar diagnosed cases) which is then used in a further matching process to find the best match. The inference is made that the new image has the same class as the best match in the selected cluster. We also assume in our model, that the classes that the expert clinician has used in diagnoses are at least partially the result of analysing images which have been seen over a long time and in a particular order.
This model allows many experiments which determine, for example, the effects of different recall ability (associated with experience) on classification of new images. We report the results of these investigations in detail and conclude that the overall approach is successful but that further research is necessary to investigate the relative effects of the various parameters on the clinical value of the classification predictions.
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Innocent, P.R., John, R.I., Barnes, M. (2000). Neuro-Fuzzy Models of Radiographic Image Classification. In: Szczepaniak, P.S., Lisboa, P.J.G., Kacprzyk, J. (eds) Fuzzy Systems in Medicine. Studies in Fuzziness and Soft Computing, vol 41. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1859-8_17
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DOI: https://doi.org/10.1007/978-3-7908-1859-8_17
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