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Classifying Cells for Cancer Diagnosis Using Neural Networks
December 1991 (vol. 6 no. 6)
pp. 8-12

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.

Citation:
Ciamac Moallemi, "Classifying Cells for Cancer Diagnosis Using Neural Networks," IEEE Intelligent Systems, vol. 6, no. 6, pp. 8-12, Dec. 1991, doi:10.1109/64.108946
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