Probabilistic neural network as chromosome classifier

This paper applies a probabilistic neural network (PNN) towards the classification of human chromosomes. The inputs to the network are thirty different features extracted from the chromosome image. The output is one of twenty-four different classes of chromosomes. The network has been tested on Cope...

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Published inIEEE, PISCATAWAY, NJ, (USA). Vol. 1, pp. 935-937. 1993 Vol. 1; pp. 935 - 938 vol.1
Main Authors Sweeney, W.P., Musavi, M.T., Guidi, J.N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1993
Subjects
Artificial neural networks
Biological cells
Cancer
Computer networks
Feature extraction
Humans
Image databases
Laboratories
Neural networks
Testing
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Summary:This paper applies a probabilistic neural network (PNN) towards the classification of human chromosomes. The inputs to the network are thirty different features extracted from the chromosome image. The output is one of twenty-four different classes of chromosomes. The network has been tested on Copenhagen, Edinburgh and Philadelphia databases. The recognition rates achieved in this study are better than conventional methods and comparable to another neural network technique, that has been previously reported.
Bibliography:SourceType-Books-1
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SourceType-Conference Papers & Proceedings-2
ISBN:0780314212
9780780314214
DOI:10.1109/IJCNN.1993.714064