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| Ciamac Moallemi, "Classifying Cells for Cancer Diagnosis Using Neural Networks," IEEE Intelligent Systems, vol. 6, no. 6, pp. 8-12, December, 1991. | |||
| BibTex | x | ||
| @article{ 10.1109/64.108946, author = {Ciamac Moallemi}, title = {Classifying Cells for Cancer Diagnosis Using Neural Networks}, journal ={IEEE Intelligent Systems}, volume = {6}, number = {6}, issn = {0885-9000}, year = {1991}, pages = {8-12}, doi = {http://doi.ieeecomputersociety.org/10.1109/64.108946}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - MGZN JO - IEEE Intelligent Systems TI - Classifying Cells for Cancer Diagnosis Using Neural Networks IS - 6 SN - 0885-9000 SP8 EP12 EPD - 8-12 A1 - Ciamac Moallemi, PY - 1991 VL - 6 JA - IEEE Intelligent Systems ER - | |||
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.