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<p>The generalization capabilities, for learned subtle image pattern categories, of neural network and algorithmic classification techniques are described. Several neural network and algorithmic techniques have been applied to a set of feature vectors extracted from thermal infrared images which characterize the extent of whiplash injury. Thermography recently has been reported to have clinical utility in a multitude of neuromusculoskeletal disorders, particularly with soft tissue injuries such as whiplash in which there are few widely agreed upon diagnostic standards. The results of this research indicate that the backpropagation neural network produces the best classification results and provides significantly better generalization from a set of training patterns. Results of unsupervised classification of the data using clustering algorithms and the Adaptive Resonance Theory (ART2) neural network demonstrate the difficulties of learning and of generalization of patterns from such data.</p>
ART2 neural network; generalization; image pattern classifiers; image pattern categories; neural network; algorithmic classification techniques; feature vectors; thermal infrared images; whiplash injury; neuromusculoskeletal disorders; soft tissue injuries; backpropagation neural network; training patterns; unsupervised classification; learning systems; neural nets; pattern recognition; picture processing

P. Goodman, V. Kaburlasos, D. Egbert and J. Witchey, "Generalization Capabilities of Subtle Image Pattern Classifiers," in IEEE Transactions on Knowledge & Data Engineering, vol. 4, no. , pp. 172-177, 1992.
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