Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Computer Vision
Expert Systems with Applications: An International Journal
Paper: Connectionist expert systems as medical decision aid
Artificial Intelligence in Medicine
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A computer-based system for diagnosing bladder cancer is described. Typically, an object falls into one of two classes: Well or Not-well. The Well class contains the cells that will actually be useful for diagnosing bladder cancer; the Not-well class includes everything else. Several descriptive features are extracted from each object in the image and then fed to a multilayer perceptron, which classifies them as Well or Not-well. The perceptron's superior classification abilities reduces the number of computer misclassification errors to a level tolerable for clinical use. Also, the perceptron's parallelism and other aspects of this implementation lend it to extremely fast computation, thus providing accurate classification at an acceptable speed.