Neural network discrimination of subtle image patterns

A report is presented on a comparison between neural network and algorithmic classification techniques applied to a specific thermography program: the analysis of image patterns which characterize the extent of whiplash injury. Thermography recently has been reported to have clinical utility in a multitude of neuromusculoskeletal disorders. Of particular import is the application of thermography to soft-tissue injuries in which there are few diagnostic gold standards. Likewise, neural networks have proven to be powerful pattern separators and classifiers in a variety of real-world problems. This is primarily due to their capabilities of pattern completion and nonlinear separability in feature space. Research results show that backpropagation neural networks can accurately classify up to 90% of the whiplash thermal images, while conventional algorithmic classifiers accurately classify only up to 75%. Slight differences were found in the classification accuracy of two commercially available backpropagation implementations