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Evaluation of artificial neural networks for fungal identification, employing morphometric data from spores of Pestalotiopsis species

Published online by Cambridge University Press:  01 August 1998

ALEXANDRA MORGAN ,
LYNNE BODDY ,
J. ELIZABETH M. MORDUE  and
COLIN W. MORRIS
ALEXANDRA MORGAN
Affiliation:
School of Pure and Applied Biology, University of Wales, Cardiff CF1 3TL, U.K.
LYNNE BODDY
Affiliation:
School of Pure and Applied Biology, University of Wales, Cardiff CF1 3TL, U.K.
J. ELIZABETH M. MORDUE
Affiliation:
International Mycological Institute, Bakeham Lane, Egham, Surrey, U.K.
COLIN W. MORRIS
Affiliation:
Department of Computer Studies, University of Glamorgan, Pontypridd CF37 1DL, U.K.
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Abstract

The relative abilities of the multilayer perceptron, radial basis function, asymmetric radial basis function and learning vector quantization artificial neural networks (ANNs) and two non-neural methods to identify fungal spores were compared. ANNs were trained on morphometric data from spores of Pestalotiopsis spp. and a few species in the related Truncatella and Monochaetia. The optimized neural and statistical classifiers had similar identification success on an unseen data set – between 76·0 and 78·8% of a 16-species group and between 63·0 and 67·7% of a 19-species group. The relative merits of each classifier are discussed, as is the potential of ANNs in mycology.

Type
Research Article
Information
Mycological Research , Volume 102 , Issue 8 , August 1998 , pp. 975 - 984
Copyright
The British Mycological Society 1998

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