Abstract
In flow cytometry, the pulse waveform features measurable by current analog instruments are limited to the pulse integral, peak, and width. Digitization 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.
Original language | English (US) |
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Pages | 2211-2216 |
Number of pages | 6 |
State | Published - 1995 |
Event | Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Aust Duration: Nov 27 1995 → Dec 1 1995 |
Other
Other | Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) |
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City | Perth, Aust |
Period | 11/27/95 → 12/1/95 |
ASJC Scopus subject areas
- Software