Classifying cells for cancer diagnosis using neural networks

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.<>