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. 2020 Jul 22;10:12142. doi: 10.1038/s41598-020-68662-3

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Delta-enhanced class detector performances and results. a Confusion matrix corresponding to each of the ten neural networks trained on the lensless microscope dataset. b Overall testing accuracy performances for each of the ten testing classes. The number used on x axis to label each species correspond to the species number in panel a. c,d DEC detector anomaly detection performances tested on in silico generated data. c Testing accuracy performances for varying percentage values of in silico species similarity with the trained species. d Example of average features space parallel coordinates plot for the in-silico species obtained using the species Spirostomum Ambiguum. By increasing the similarity, the features of the surrogate species approach the features of the real species, resulting in an increased average anomaly misclassification rate, decreasing the overall accuracy levels. e Detection of unknown species. The panel shows the percentage of samples detected by all the DEC detectors as anomaly, when removing one training species from the set, for each of the ten training species. These numbers reflect the level of accuracy of the proposed algorithm in detecting unseen species. The number used on x axis to label each species correspond to the species number in panel a.