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. 2022 Mar 2;5:23. doi: 10.1038/s41746-022-00571-3

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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 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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Fig. 1 — The green dotted line boxes refer to the ground truth of the eyelid tumors. The orange solid line boxes refer to the detection results by Faster region-based convolutional neural network (R-CNN). The orange numerical values indicate confidence scores (range 0–1) which reflect how confident the model is that the box contains an eyelid tumor. a Malignant eyelid tumor: a1 basal cell carcinomas. a2 squamous cell carcinomas. a3 sebaceous gland carcinoma. b Benign eyelid tumor: b1 nevus. b2 seborrheic keratosis. b3 squamous cell papilloma.