The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus
Active Shape Models (ASM) are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to classify each species to their true species type. ASM is used as a feature extraction tool to select information from hook images that can be used as inp...
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ftnapieruniv:oai:repository@napier.ac.uk:1793297 2023-05-15T15:32:20+02:00 The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus Ali, Rozniza Hussain, Amir Bron, James E. Shinn, Andrew P. 2012-12-31 https://doi.org/10.1007/978-3-642-34478-7_32 http://researchrepository.napier.ac.uk/Output/1793297 unknown Springer http://researchrepository.napier.ac.uk/Output/1793297 doi:https://doi.org/10.1007/978-3-642-34478-7_32 9783642344770 10.1007/978-3-642-34478-7_32 Attachment hooks image processing SEM parasite machine learning classifier Conference Proceeding 2012 ftnapieruniv https://doi.org/10.1007/978-3-642-34478-7_32 2023-01-12T23:43:06Z Active Shape Models (ASM) are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to classify each species to their true species type. ASM is used as a feature extraction tool to select information from hook images that can be used as input data into trained classifiers. Linear (i.e. LDA and KNN) and non-linear (i.e. MLP and SVM) models are used to classify Gyrodactylus species. Species of Gyrodactylus, ectoparasitic monogenetic flukes of fish, are difficult to discriminate and identify on morphology alone and their speciation currently requires taxonomic expertise. The current exercise sets out to confidently classify species, which in this example includes a species which is notifiable pathogen of Atlantic salmon, to their true class with a high degree of accuracy. The findings from the current exercise demonstrates that data subsequently imported into a K-NN classifier, outperforms several other methods of classification (i.e. LDA, MLP and SVM) that were assessed, with an average classification accuracy of 98.75%. Conference Object Atlantic salmon Edinburgh Napier Repository (Napier University Edinburgh) 256 263 |
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Edinburgh Napier Repository (Napier University Edinburgh) |
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topic |
Attachment hooks image processing SEM parasite machine learning classifier |
spellingShingle |
Attachment hooks image processing SEM parasite machine learning classifier Ali, Rozniza Hussain, Amir Bron, James E. Shinn, Andrew P. The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus |
topic_facet |
Attachment hooks image processing SEM parasite machine learning classifier |
description |
Active Shape Models (ASM) are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to classify each species to their true species type. ASM is used as a feature extraction tool to select information from hook images that can be used as input data into trained classifiers. Linear (i.e. LDA and KNN) and non-linear (i.e. MLP and SVM) models are used to classify Gyrodactylus species. Species of Gyrodactylus, ectoparasitic monogenetic flukes of fish, are difficult to discriminate and identify on morphology alone and their speciation currently requires taxonomic expertise. The current exercise sets out to confidently classify species, which in this example includes a species which is notifiable pathogen of Atlantic salmon, to their true class with a high degree of accuracy. The findings from the current exercise demonstrates that data subsequently imported into a K-NN classifier, outperforms several other methods of classification (i.e. LDA, MLP and SVM) that were assessed, with an average classification accuracy of 98.75%. |
format |
Conference Object |
author |
Ali, Rozniza Hussain, Amir Bron, James E. Shinn, Andrew P. |
author_facet |
Ali, Rozniza Hussain, Amir Bron, James E. Shinn, Andrew P. |
author_sort |
Ali, Rozniza |
title |
The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus |
title_short |
The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus |
title_full |
The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus |
title_fullStr |
The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus |
title_full_unstemmed |
The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus |
title_sort |
use of asm feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus |
publisher |
Springer |
publishDate |
2012 |
url |
https://doi.org/10.1007/978-3-642-34478-7_32 http://researchrepository.napier.ac.uk/Output/1793297 |
genre |
Atlantic salmon |
genre_facet |
Atlantic salmon |
op_relation |
http://researchrepository.napier.ac.uk/Output/1793297 doi:https://doi.org/10.1007/978-3-642-34478-7_32 9783642344770 10.1007/978-3-642-34478-7_32 |
op_doi |
https://doi.org/10.1007/978-3-642-34478-7_32 |
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256 |
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263 |
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1766362846936956928 |