• Opto-Electronic Engineering
  • Vol. 50, Issue 1, 220199 (2023)
Liming Liang*, Xin Dong, Renjie Li, and Anjun He
Author Affiliations
  • School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
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    DOI: 10.12086/oee.2023.220199 Cite this Article
    Liming Liang, Xin Dong, Renjie Li, Anjun He. Classification algorithm of retinopathy based on attention mechanism and multi feature fusion[J]. Opto-Electronic Engineering, 2023, 50(1): 220199 Copy Citation Text show less
    References

    [3] Zhou K, Gu Z W, Liu W, et al. Multi-cell multi-task convolutional neural networks for diabetic retinopathy grading[C]//Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018: 2724–2727. https://doi.org/10.1109/EMBC.2018.8512828.

    [4] Du R Y, Chang D L, Bhunia A K, et al. Fine-grained visual classification via progressive multi-granularity training of jigsaw patches[C]//16th European Conference on Computer Vision, 2020: 153–168. https://doi.org/10.1007/978-3-030-58565-5_10.

    [7] Zhang H, Wu C R, Zhang Z Y, et al. ResNeSt: split-attention networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022: 2735–2745. https://doi.org/10.1109/CVPRW56347.2022.00309.

    [9] Shao Q B, Gong L J, Ma K, et al. Attentive CT lesion detection using deep pyramid inference with multi-scale booster[C]//22nd International Conference on Medical Image Computing and Computer Assisted Intervention, 2019: 301–309. https://doi.org/10.1007/978-3-030-32226-7_34.

    [12] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[Z]. arXiv: 1609.02907, 2016. https://arxiv.org/abs/1609.02907v3.

    [13] Li Q M, Han Z C, Wu X M. Deeper insights into graph convolutional networks for semi-supervised learning[C]//32nd AAAI Conference on Artificial Intelligence, 2018: 3538-3545.

    [14] Li Y, Gupta A. Beyond grids: learning graph representations for visual recognition[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems, 2018: 9245–9255. https://doi.org/10.5555/3327546.3327596.

    [15] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2999–3007. https://doi.org/10.1109/ICCV.2017.324.

    [20] Song J W, Yang R Y. Feature boosting, suppression, and diversification for fine-grained visual classification[C]//2021 International Joint Conference on Neural Networks (IJCNN), 2021: 1–8. https://doi.org/10.1109/IJCNN52387.2021.9534004.

    [21] Shi L, Zhang J X. Few-shot learning based on multi-stage transfer and class-balanced loss for diabetic retinopathy grading[Z]. arXiv: 2109.11806, 2021. https://arxiv.org/abs/2109.11806.

    [23] Thota N B, Reddy D U. Improving the accuracy of diabetic retinopathy severity classification with transfer learning[C]//2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), 2020: 1003–1006. https://doi.org/10.1109/MWSCAS48704.2020.9184473.

    Liming Liang, Xin Dong, Renjie Li, Anjun He. Classification algorithm of retinopathy based on attention mechanism and multi feature fusion[J]. Opto-Electronic Engineering, 2023, 50(1): 220199
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