Nematode identification using artificial neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

Nematodes are microscopic, worm-like organisms with applications in monitoring the environment for potential ecosystem damage or recovery. Nematodes are an extremely abundant and diverse organism, with millions of different species estimated to exist. This trait leads to the task of identifying nematodes, at a species level, being complicated and time-consuming. Their morphological identification process is fundamentally one of pattern matching, using sketches in a standard taxonomic key as a comparison to the nematode image under a microscope. As Deep Learning has shown vast improvements, in particular, for image classification, we explore the effectiveness of Nematode Identification using Convolutional Neural Networks. We also seek to discover the optimal training process and hyper-parameters for our specific context.

Original languageEnglish
Title of host publicationDeLTA 2020 - Proceedings of the 1st International Conference on Deep Learning Theory and Applications
EditorsAna Fred, Kurosh Madani
PublisherSciTePress
Pages13-22
Number of pages10
ISBN (Electronic)9789897584411
Publication statusPublished - 2020
Event1st International Conference on Deep Learning Theory and Applications, DeLTA 2020 - Virtual, Online
Duration: 08 Jul 202010 Jul 2020

Publication series

NameDeLTA 2020 - Proceedings of the 1st International Conference on Deep Learning Theory and Applications

Conference

Conference1st International Conference on Deep Learning Theory and Applications, DeLTA 2020
CityVirtual, Online
Period08/07/202010/07/2020

Keywords

  • Convolutional Neural Networks
  • Image Classification

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