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. 2022 Apr 27;13:862702. doi: 10.3389/fimmu.2022.862702

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Copyright © 2022 Liu, Huang, Sun, Li, Luo, Cui, Zhu, Bi, Chen and Liu

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Comparison of the Unet network with artificial interpretation results for full convolutional attentional enhancement. (A) Comparative images of positive retinal ganglion cell numbers in control and model group. (B–F) Comparative images of the positive rate of the retinal layer area in control and model group. (G–J) Comparative images of positive rate of retinal thickness in control and model group. GT is the result of manual interpretation; L-R is the result of low-resolution image AI interpretation; H-R is the result of AI interpretation of high resolution image. The Control group for each method is considered 100% vs. control group: *p < 0.05, **p < 0.01, ***p < 0.001.