[2] [2] GUO L, YANG J J, PENG L, et al. A computer-aided healthcare system for cataract classification and grading based on fundus image analysis[J]. Computers in Industry, 2015, 69: 72-80.

[3] [3] DONG Y, ZHANG Q, QIAO Z, et al. Classification of cataract fundus image based on deep learning[C]//2017 IEEE International Conference on Imaging Systems and Techniques (IST), October 18-20, 2017, Beijing, China. New York: IEEE, 2017: 1-5.

[4] [4] ZHANG H, NIU K, XIONG Y, et al. Automatic cataract grading methods based on deep learning[J]. Comput Methods Programs Biomed, 2019, 182: 104978.

[5] [5] RAMANI R G, SHANTHAMALAR J J. Improved image processing techniques for optic disc segmentation in retinal fundus images[J]. Biomedical Signal Processing and Control, 2020, 58: 101832.

[6] [6] WANG J L, LI Y J, YANG K F J C I B, et al. Retinal fundus image enhancement with image decomposition and visual adaptation[J]. Computers in Biology and Medicine, 2021, 128: 104116.

[9] [9] LIU X, JIANG J, ZHANG K, et al. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network[J]. PloS One, 2017, 12(3): e0168606.

[10] [10] HU S, WANG X, WU H, et al. Unified diagnosis framework for automated nuclear cataract grading based on smartphone slit-lamp images[J]. IEEE Access, 2020, 8: 174169-174178.

[11] [11] KEENAN T D, CHEN Q, AGRN E, et al. DeepLensNet: deep learning automated diagnosis and quantitative classification of cataract type and severity[J]. Ophthalmology, 2022, 129(5): 571-584.

[12] [12] SAHU S, SINGH A K, GHRERA S, et al. An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE[J]. Optics Laser Technology, 2019, 110: 87-98.

[13] [13] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition, June 26-July 1, 2016, Las Vegas, USA. New York: IEEE, 2016: 770-778.

[14] [14] WANG Q L, WU B G, ZHU P F, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]//2020 IEEE Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, USA. New York: IEEE, 2020: 11534-11542.

[15] [15] IN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//IEEE International Conference on Computer Vision, October 22-29, 2017, Venice, USA. New York: IEEE, 2017: 2980-2988.

[16] [16] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, June 18-21, 2018, Salt Lake City, USA. New York: IEEE, 2018: 7132-7141.

[17] [17] LIU Z, MAO H, WU C Y, et al. A convnet for the 2020s[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 19-24, 2022, New Orleans, USA. New York: IEEE, 2022: 11966-11976.

[18] [18] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, USA. New York: IEEE, 2017: 4700-4708.

[19] [19] SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-cam: visual explanations from deep networks via gradient-based localization[C]//IEEE International Conference on Computer Vision, October 22-29, 2017, Venice, USA. New York: IEEE, 2017: 618-626.