Classification of Fish Ectoparasite Genus Gyrodactylus SEM Images Using ASM and Complex Network Model

Ali, Rozniza; Jiang, Bo; Man, Mustafa; Hussain, Amir; Luo, Bin

Abstract

Active Shape Models and Complex Network method 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 K-NN) 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 results show that Multi-Layer Perceptron (MLP) is the best classifier for performing the initial classification of Gyrodactylus species, with an average of 98.36%. Using MLP classifier, only one species has been misallocated. It is essential, therefore, to employ a method that does not generate type I or type II misclassifications where G. salaris is concerned. In comparison, only K-NN classifier has managed to to achieve full classification on the G. salaris.

Citation

Ali, R., Jiang, B., Man, M., Hussain, A., & Luo, B. (2014, November). Classification of Fish Ectoparasite Genus Gyrodactylus SEM Images Using ASM and Complex Network Model. Presented at 21st International Conference on Neural Information Processing, ICONIP 2014, Kuching, Malaysia

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 21st International Conference on Neural Information Processing, ICONIP 2014
• Start Date: Nov 3, 2014
• End Date: Nov 6, 2014
• Publication Date: 2014
• Deposit Date: Sep 27, 2019
• Publisher: Springer
• Volume: 8836
• Pages: 103-110
• Series Title: Lecture Notes in Computer Science
• Series Number: 8836
• Series ISSN: 0302-9743
• Book Title: Neural Information Processing: 21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part III
• ISBN: 978-3-319-12642-5
• DOI: https://doi.org/10.1007/978-3-319-12643-2_13
• Keywords: Gyrodactylus; classification; Active Shape Model; Complex Network
• Public URL: http://researchrepository.napier.ac.uk/Output/1793021