NDLI: Neural network discrimination of subtle image patterns

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International 1989 Joint Conference on Neural Networks

Neural network discrimination of subtle image patterns

Content Provider	IEEE Xplore Digital Library
Author	Egbert, D.D. Kaburlasos, V.G. Goodman, P.H.
Copyright Year	1990
Abstract	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
Starting Page	517
Ending Page	524
File Size	633476
Page Count	8
File Format	PDF
DOI	10.1109/IJCNN.1990.137616
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1990-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article

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