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Competitive fitness analysis using Convolutional Neural Network Cover

Competitive fitness analysis using Convolutional Neural Network

Open Access
|Nov 2020

Figures & Tables

Figure 1:

Images with GFP (yellow) and non-GFP (green) animals marked by the CNN model.
Images with GFP (yellow) and non-GFP (green) animals marked by the CNN model.

Figure S1:

Pictures that rendered: the most extreme proportion differences (+) on the first panel, the most extreme proportion differences (−) second panel, no difference on the last panel.
Pictures that rendered: the most extreme proportion differences (+) on the first panel, the most extreme proportion differences (−) second panel, no difference on the last panel.

Figure 2:

(A) Correct detection of worms and the number of errors at increasing concentrations of animals, (B) Close-up of error types at increasing animal density.
(A) Correct detection of worms and the number of errors at increasing concentrations of animals, (B) Close-up of error types at increasing animal density.

Figure 3:

(A) Boxplot of the frequency of focal animals for the two methods, (B) Boxplot of the standard deviation of the proportion of focals for the two methods.
(A) Boxplot of the frequency of focal animals for the two methods, (B) Boxplot of the standard deviation of the proportion of focals for the two methods.

Figure S2:

Variability of measures of competitive fitness. Plots of measures of variability (y-axis) vs. measures of competitive fitness (x-axis). Panel A show the plots for the model, while panel B, for the plots for count ‘by eye’. Panels show the frequency of the focal animals as the measure of competitive fitness.
Variability of measures of competitive fitness. Plots of measures of variability (y-axis) vs. measures of competitive fitness (x-axis). Panel A show the plots for the model, while panel B, for the plots for count ‘by eye’. Panels show the frequency of the focal animals as the measure of competitive fitness.

Performance metrics of the CNN model computed on the evaluation set for high animal densities (above 70)_

Area
MetricSmallMediumLargeAll
Average precision @ IoU = 0.50No animals0.7840.8100.787
Average recall @ IoU = 0.50No animals0.8510.8560.842

Performance metrics of the CNN model computed on the evaluation set for low and moderate animal densities (below 70)_

Area
MetricSmallMediumLargeAll
Average precision @ IoU = 0.50No animals0.8700.8830.872
Average recall @ IoU = 0.50No animals0.9300.9180.917
DOI: https://doi.org/10.21307/jofnem-2020-108 | Journal eISSN: 2640-396X | Journal ISSN: 0022-300X
Language: English
Page range: 1 - 15
Published on: Nov 6, 2020
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2020 Joanna K. Palka, Krzysztof Fiok, Weronika Antoł, Zofia M. Prokop, published by Society of Nematologists, Inc.
This work is licensed under the Creative Commons Attribution 4.0 License.