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首页> 外文会议>International conference on neural information processing >The Use of ASM Feature Extraction and Machine Learning for the Discrimination of Members of the Fish Ectoparasite Genus Gyrodactylus
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The Use of ASM Feature Extraction and Machine Learning for the Discrimination of Members of the Fish Ectoparasite Genus Gyrodactylus

机译：利用ASM特征提取和机器学习来区分鱼类寄生虫属Gyrodactylus属成员

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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%.
机译：主动形状模型（ASM）被应用于包括触须病原体G. salaris在内的几种陀螺的附着钩，以将每种物种分类为它们的真实物种类型。 ASM用作特征提取工具，可以从挂钩图像中选择信息，这些信息可以用作经过训练的分类器中的输入数据。线性模型（即LDA和KNN）和非线性模型（即MLP和SVM）用于对陀螺菌种进行分类。鱼类的寄生寄生单基因（Gyrodactylus）种类很难单独辨别和鉴定，其形态目前需要分类学专门知识。当前的工作是对物种进行可靠地分类，在本示例中，该物种包括可作为大西洋鲑鱼的应报告病原体的物种，其分类准确度很高。当前练习的结果表明，随后导入K-NN分类器的数据要优于其他几种评估的分类方法（即LDA，MLP和SVM），平均分类准确度为98.75％。

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