Journal of Optoelectronics · Laser, Volume. 35, Issue 6, 650(2024)

Keratoconus assisted diagnosis based on SOANet network

LI Mingyue1, LIU Fenglian1, LI Jing1, WANG Riwei2, and TANG Zuoping2、*
Author Affiliations
  • 1Key Laboratory of Computer Vision and Systems, Ministry of Education, Tianjin Key Laboratory of Intelligent Computing and Software New Technology, Tianjin University of Technology, Tianjin 300384, China
  • 2Zhejiang Women's Science and Technology Innovation Studios, Wenzhou University of Technology, Wenzhou, Zhejiang 325035, China
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    References(11)

    [4] [4] LIN S R, LADAS J G, BAHADUR G G, et al. A review of machine learning techniques for keratoconus detection and refractive surgery screening[J]. Seminars in Ophthalmology, 2019, 34(4): 317-326.

    [5] [5] HALLTT N, YI K, DICK J, et al. Deep learning based unsupervised and semi-supervised classification for keratoconus[C]//2020 International Joint Conference on Neural Networks (IJCNN), July 19-24, 2020, Glasgow, England, United Kingdom. New York: IEEE, 2020: 1-7.

    [6] [6] FENG R, XU Z, ZHENG X, et al. KerNet: a novel deep learning approach for keratoconus and sub-clinical keratoconus detection based on raw data of the pentacam HR system[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 25(10): 3898-3910.

    [7] [7] LAVRIC A, POPA V, TAKAHASHI H, et al. Detecting keratoconus from corneal imaging data using machine learning[J]. IEEE Access, 2020, 8: 149113-149121.

    [8] [8] CASTRO-LUNA G, JIMNEZ-RODRGUEZ D, CASTAO-FERNNDEZ A B, et al. Diagnosis of subclinical keratoconus based on machine learning techniques[J]. Journal of Clinical Medicine, 2021, 10(18): 4281.

    [9] [9] GAIROLA S, JOSHI P, BALASUBRAMANIAM A, et al. Keratoconus classifier for smartphone-based corneal topographer[C]//2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 11-15, 2022, Glascow, Scotland. New York: IEEE, 2022: 1875-1878.

    [10] [10] SHI C, WANG M, ZHU T, et al. Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities[J]. Eye and Vision, 2020, 7(1): 48.

    [13] [13] LI J, CHEN B M, LEE G H. So-Net: Self-organizing network for point cloud analysis[C]//IEEE Conference on Computer Vision and Pattern Recognition, July 18-23, 2018, Salt Lake City, USA. Piscataway, NJ: IEEE, 2018: 9397-9406.

    [14] [14] AKWENSI P H, WANG R. Attention-based multi-scale graph convolution for point cloud semantic segmentation[C]//IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, July 17-22, 2022, Kuala Lumpur, Malaysia. New York: IEEE, 2022: 7515-7518.

    [15] [15] GUO M H, CAI J X, LIU Z N, et al. PCT: Point cloud transformer[J]. Computational Visual Media, 2021, 7: 187-199.

    [17] [17] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//31st International Conference on Neural Information Processing Systems, December 04-09, 2017, Long Beach, California. Red Hook: Curran Associates Inc, 2017: 6000-6010.

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    LI Mingyue, LIU Fenglian, LI Jing, WANG Riwei, TANG Zuoping. Keratoconus assisted diagnosis based on SOANet network[J]. Journal of Optoelectronics · Laser, 2024, 35(6): 650

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    Paper Information

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    Received: Aug. 12, 2023

    Accepted: Dec. 13, 2024

    Published Online: Dec. 13, 2024

    The Author Email: TANG Zuoping (tanzp@wzu.edu.cn)

    DOI:10.16136/j.joel.2024.06.0431

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