Classifying cells for cancer diagnosis using neural networks
- 1 December 1991
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Expert
- Vol. 6 (6) , 8-12
- https://doi.org/10.1109/64.108946
Abstract
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.Keywords
This publication has 1 reference indexed in Scilit:
- Parallel Distributed ProcessingPublished by MIT Press ,1986