Nematode Identification using Artificial Neural Networks

Jason Uhlemann, Oisin Cawley, Thomais Kakouli-Duarte

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.

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