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. 2022 Jun 15;12:9925. doi: 10.1038/s41598-022-14140-x

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© The Author(s) 2022

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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Training and validation results. The training loss of Sets 1–7 approached zero. In Set 1, images of right and left eyes were easily distinguished, with the validation loss approaching zero and the validation accuracy thus approaching 1.0. In Sets 2–5, similar validation loss (~ 0.4) and validation accuracy (~ 90%) were obtained. For Set 6, which consisted of vertical images, the accuracy was slightly inferior compared to Sets 2–5. Set 7 showed the second-highest validation accuracy. In contrast to Sets 1–7, overfitting was observed in Set 8; in this set, training loss converged whereas validation loss diverged. The validation accuracy of Set 8 was around 0.5 and failed to improve over the learning period. OD oculus dexter, OS oculus sinister, H horizontal, V vertical.