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. 2021 Apr 12;11:7995. doi: 10.1038/s41598-021-86995-5

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© The Author(s) 2021

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Multiclass classification of the ALL dataset using 200 training images. (a) Decrease of validation and training loss during training of one of the ten folds. Classification of the test set results in an F1-score of 0.81 ± 0.09 (mean ± s.d., n = 10 folds). (b) Confusion matrix visualizing the percentage of network predictions in each class. On the right hand side of the matrix the number of images in each class is given. In each class, 50% (in the case of atypical lymphocytes) or more cell images are classified correctly. Atypical lymphocytes (n = 12) and monocytes (n = 5) have the lowest number of images and show the most misclassifications.