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Classifying cells for cancer diagnosis using neural networks

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1 Author(s)
Moallemi, C. ; MIT, Cambridge, MA, USA

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.<>

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IEEE Expert  (Volume:6 ,  Issue: 6 )