Pattern classification with artificial neural networks: classification of algae, based upon flow cytometer data

✍ Scribed by J.R.M. Smits; L.W. Breedveld; M.W.J. Derksen; G. Kateman; H.W. Balfoort; J. Snoek; J.W. Hofstraat

• Publisher: Elsevier Science
• Year: 1992
• Tongue: English
• Weight: 942 KB
• Volume: 258
• Category: Article
• ISSN: 0003-2670
• DOI: 10.1016/0003-2670(92)85193-a

✦ Synopsis

In this paper the applicability of artificial neural networks as pattern classifiers is investigated To study the behaviour of neural networks as pattern classifier for complex data, the identification and counting of phytoplankton, based upon flow cytometer data, is taken as an example For this problem most conventional pattern recognition techniques fail, due to the shape of the clusters in the data Three experiments have been carved out First, it is investigated whether artificial neural network systems are capable of discriminating between two different classes of algal species (poisonous species and non-poisonous species) Second, it is tested whether neural network systems can be used for identification of algal species Third, the robustness of neural network systems towards changes in the flow cytometer settings has been studied The results of these experiments show that a neural network system may be used as a pattern classifier