Classification of chromosomes using a probabilistic neural network.
This paper describes the application of a probabilistic neural network (PNN) to the classification of normal human chromosomes. The inputs to the network are 30 different features extracted from each chromosome in digitized images of metaphase spreads. The output is 1 of 24 different classes of chromosomes (the 22 autosomes plus the sex chromosomes X and Y). An updating procedure was implemented to take advantage of the fact that in a normal somatic cell only two chromosomes can be assigned to each class. The network has been tested using the Copenhagen, Edinburgh, and Philadelphia databases of digitized images of human chromosomes. The recognition rates achieved in this study are superior to those reported using either the maximum likelihood or back propagation neural network techniques.
University of Maine, Department of Electrical and Computer Engineering, Orono 04469-5708.
W P
WP
Sweeney
M T
MT
Musavi
J N
JN
Guidi
D002877:Chromosomes, Human / Q000145:classification*; D016208:Databases, Factual; D006801:Humans; D007621:Karyotyping; D016571:Neural Networks, Computer; D011336:Probability