Issue No. 06 - December (1991 vol. 6)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/64.108946
<p>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.</p>
C. Moallemi, "Classifying Cells for Cancer Diagnosis Using Neural Networks," in IEEE Intelligent Systems, vol. 6, no. , pp. 8-12, 1991.