DocumentCode :
295867
Title :
Neural network analysis of digital flow cytometric data
Author :
Godavarti, Mahesh ; Rodríguez, Jeffrey J. ; Yopp, Timothy A. ; Lambert, Georgina M. ; Galbraith, David W.
Author_Institution :
Dept. of Electr. & Comput. Eng., Arizona Univ., Tucson, AZ, USA
Volume :
5
fYear :
1995
fDate :
Nov/Dec 1995
Firstpage :
2211
Abstract :
In flow cytometry, the pulse waveform features measurable by current analog instruments are limited to the pulse integral, peak, and width. Digitalization of the waveforms provides a means for the extraction of additional features, such as skewness, kurtosis, and Fourier properties. The introduction of additional features requires automated procedures for classification of biological particles. In this work, we implemented and evaluated neural network classification algorithms using derived, complex features, as well as using the raw, sampled data without feature extraction. The performance of the neural networks was compared with that of a more conventional means of classification in flow cytometry, the K-means clustering algorithm
Keywords :
biology computing; cellular biophysics; feature extraction; multilayer perceptrons; pattern classification; self-organising feature maps; Fourier properties; Kohonen network; biological cells; biological particles; digital flow cytometric data; digital waveforms; feature extraction; kurtosis; neural network classification; performance evaluation; pulse waveform; skewness; Clustering algorithms; Discrete Fourier transforms; Feature extraction; Instruments; Neural networks; Optical pulses; Optical scattering; Pulse amplifiers; Pulse measurements; Space vector pulse width modulation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
Type :
conf
DOI :
10.1109/ICNN.1995.487704
Filename :
487704
Link To Document :
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