Publication Date:
2021-05-19
Description:
Over the last two decades, improvements in developing computational tools have
made significant contributions to the classification of images of biological
specimens to their corresponding species. These days, identification of biological
species is much easier for taxonomists and even non-taxonomists due to the
development of automated computer techniques and systems. In this study, we
developed a fully automated identification model for monogenean images based
on the shape characters of the haptoral organs of eight species:
Sinodiplectanotrema malayanum, Diplectanum jaculator, Trianchoratus
pahangensis, Trianchoratus lonianchoratus, Trianchoratus malayensis,
Metahaliotrema ypsilocleithru, Metahaliotrema mizellei and Metahaliotrema
similis. Linear Discriminant Analysis (LDA) method was used to reduce the
dimension of extracted feature vectors which were then used in the classification
with K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN)
classifiers for the identification of monogenean specimens of eight species. The
need for the discovery of new characters for identification of species has been
acknowledged for log by systematic parasitology. Using the overall form of
anchors and bars for extraction of features led to acceptable results in automated
classification of monogeneans. To date, this is the first fully automated
identification model for monogeneans with an accuracy of 86.25% using KNN
and 93.1% using ANN.
Description:
Published
Keywords:
Monogenean
;
Morphology
;
Fish parasite
;
Artificial neural networks
;
K-nearest neighbor
;
Identification
;
Sinodiplectanotrema malayanum
;
Diplectanum jaculator
;
Trianchoratus pahangensis
;
Trianchoratus lonianchoratus
;
Trianchoratus malayensis
;
Metahaliotrema ypsilocleithru
;
Metahaliotrema mizellei
;
Metahaliotrema similis
Repository Name:
AquaDocs
Type:
Journal Contribution
,
Refereed
Format:
pp.805-820
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