Y. Sun et al., “Dialysis-associated hyperglycemia: manifestations and treatment,” Int Urol Nephrol, vol. 52, no. 3, pp. 505–517, Mar. 2020, https://doi.org/10.1007/s11255-019-02373-1.

Article  PubMed  CAS  Google Scholar 

Y. Zeng et al., “Retinal vasculature–function correlation in non-proliferative diabetic retinopathy,” Doc Ophthalmol, vol. 140, no. 2, pp. 129–138, Apr. 2020, https://doi.org/10.1007/s10633-019-09724-4.

Article  PubMed  Google Scholar 

C. Bhardwaj, S. Jain, and M. Sood, “Hierarchical severity grade classification of non-proliferative diabetic retinopathy,” J Ambient Intell Human Comput, vol. 12, no. 2, pp. 2649–2670, Feb. 2021, https://doi.org/10.1007/s12652-020-02426-9.

Article  Google Scholar 

M. J. Davies et al., “Management of Hyperglycemia in Type 2 Diabetes, 2022. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD),” Diabetes Care, vol. 45, no. 11, pp. 2753–2786, Nov. 2022, https://doi.org/10.2337/dci22-0034.

W. Matuszewski, E. Bandurska-Stankiewicz, R. Modzelewski, U. Kamińska, and M. Stefanowicz-Rutkowska, “Diagnosis and treatment of diabetic retinopathy — historical overview,” Clinical Diabetology, vol. 6, no. 5, Art. no. 5, 2017, https://doi.org/10.5603/DK.2017.0030.

S. Wang, Y. Zuo, N. Wang, and B. Tong, “Fundus fluorescence Angiography in diagnosing diabetic retinopathy,” Pakistan Journal of Medical Sciences, vol. 33, no. 6, p. 1328, Dec. 2017, https://doi.org/10.12669/pjms.336.13405.

Article  PubMed  PubMed Central  Google Scholar 

S. Wang, L. Dong, and H. Zhang, “Comparison of fundus fluorescein angiography and fundus photography grading criteria for early diabetic retinopathy,” International Journal of Ophthalmology, vol. 15, no. 2, p. 261, 2022, https://doi.org/10.18240/ijo.2022.02.11.

Article  PubMed  PubMed Central  Google Scholar 

X. Sun et al., “A Clinical Classification of Cervical Ossification of the Posterior Longitudinal Ligament to Guide Surgical Strategy,” Spine, no. 4, p. 49, 2024, https://doi.org/10.1097/BRS.0000000000004878.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017, https://doi.org/10.1145/3065386.

Article  Google Scholar 

A. Vaswani et al., “Attention is All you Need,” in Advances in Neural Information Processing Systems, Curran Associates, Inc., 2017.

Y. Gu et al., “A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning,” Computers in Biology and Medicine, vol. 137, p. 104806, Oct. 2021, https://doi.org/10.1016/j.compbiomed.2021.104806.

Article  PubMed  Google Scholar 

H. Jin, C. Yu, Z. Gong, R. Zheng, Y. Zhao, and Q. Fu, “Machine learning techniques for pulmonary nodule computer-aided diagnosis using CT images: A systematic review,” Biomedical Signal Processing and Control, vol. 79, p. 104104, Jan. 2023, https://doi.org/10.1016/j.bspc.2022.104104.

Article  Google Scholar 

D. Sinwar et al., “Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey,” Contrast Media & Molecular Imaging, vol. 2022, p. e1306664, Oct. 2022, https://doi.org/10.1155/2022/1306664.

J. Chen et al., “TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation,” Feb. 08, 2021, arXiv: arXiv:2102.04306. https://doi.org/10.48550/arXiv.2102.04306.

H. Cao et al., “Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation,” May 12, 2021, arXiv: arXiv:2105.05537. https://doi.org/10.48550/arXiv.2105.05537.

A. Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” CoRR, vol. abs/2010.11929, 2020, [Online]. Available: https://arxiv.org/abs/2010.11929

C. Ge et al., “Advancing Vision Transformers with Group-Mix Attention,” arXiv.org. Accessed: Jun. 13, 2024. [Online]. Available: https://arxiv.longhoe.net/abs/2311.15157v1

J. Yang, C. Li, X. Dai, and J. Gao, “Focal Modulation Networks,” Advances in Neural Information Processing Systems, vol. 35, pp. 4203–4217, Dec. 2022.

Z. Liu et al., “Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows,” presented at the Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10012–10022.

C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, Sep. 1995, https://doi.org/10.1007/BF00994018.

Article  Google Scholar 

R. Bernardes et al., “Computer-Assisted Microaneurysm Turnover in the Early Stages of Diabetic Retinopathy,” Ophthalmologica, vol. 223, no. 5, pp. 284–291, Apr. 2009, https://doi.org/10.1159/000213638.

A. Deka and K. K. Sarma, “SVD and PCA features for ANN based detection of diabetes using retinopathy,” in Proceedings of the CUBE International Information Technology Conference, in CUBE ’12. New York, NY, USA: Association for Computing Machinery, Sep. 2012, pp. 38–41. https://doi.org/10.1145/2381716.2381725.

S. Roychowdhury, D. D. Koozekanani, and K. K. Parhi, “DREAM: Diabetic Retinopathy Analysis Using Machine Learning,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 5, pp. 1717–1728, Sep. 2014, https://doi.org/10.1109/JBHI.2013.2294635.

V. Gulshan et al., “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA, vol. 316, no. 22, pp. 2402–2410, Dec. 2016, https://doi.org/10.1001/jama.2016.17216.

Article  PubMed  Google Scholar 

G. Zhang et al., “Diabetic Retinopathy Grading by Deep Graph Correlation Network on Retinal Images Without Manual Annotations,” Frontiers in Medicine, vol. 9, 2022.

L. Li et al., “A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection,” IEEE Transactions on Medical Imaging, vol. 39, no. 2, pp. 413–424, 2020, https://doi.org/10.1109/TMI.2019.2927226.

Article  PubMed  Google Scholar 

E. Long et al., “An artificial intelligence platform for the multihospital collaborative management of congenital cataracts,” Nature Biomedical Engineering, vol. 1, no. 2, p. 0024, Jan. 2017, https://doi.org/10.1038/s41551-016-0024.

G. Sun, X. Liu, and X. Yu, “Multi-path cascaded U-net for vessel segmentation from fundus fluorescein angiography sequential images,” Computer Methods and Programs in Biomedicine, vol. 211, p. 106422, Nov. 2021, https://doi.org/10.1016/j.cmpb.2021.106422.

Article  PubMed  Google Scholar 

M. Chen et al., “Automatic detection of leakage point in central serous chorioretinopathy of fundus fluorescein angiography based on time sequence deep learning,” Graefes Arch Clin Exp Ophthalmol, vol. 259, no. 8, pp. 2401–2411, Aug. 2021, https://doi.org/10.1007/s00417-021-05151-x.

Z. Gao et al., “End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning,” Graefes Arch Clin Exp Ophthalmol, vol. 260, no. 5, pp. 1663–1673, May 2022, https://doi.org/10.1007/s00417-021-05503-7.

Article  PubMed  Google Scholar 

Z. Gao et al., “Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning,” Brit J Ophthalmol, vol. 107, no. 12, pp. 1852–1858, 2023, https://doi.org/10.1136/bjo-2022-321472.

Article  Google Scholar 

X. Pan et al., “Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning,” Graefes Arch Clin Exp Ophthalmol, vol. 258, no. 4, pp. 779–785, Apr. 2020, https://doi.org/10.1007/s00417-019-04575-w.

M. Waqar, H. Dawood, H. Dawood, N. Majeed, A. Banjar, and R. Alharbey, “An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction,” Scientific Programming, vol. 2021, p. e6621622, Mar. 2021, https://doi.org/10.1155/2021/6621622.

S. Ren, X. Yang, S. Liu, and X. Wang, “SG-Former: Self-guided Transformer with Evolving Token Reallocation,” presented at the Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 6003–6014.

B. Kurt, V. V. Nabiyev, and K. Turhan, “Medical images enhancement by using anisotropic filter and CLAHE,” in 2012 International Symposium on Innovations in Intelligent Systems and Applications, Jul. 2012, pp. 1–4. https://doi.org/10.1109/INISTA.2012.6246971.

M. K. Uçar, M. Nour, H. Sindi, and K. Polat, “The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets,” Mathematical Problems in Engineering, vol. 2020, p. e2836236, May 2020, https://doi.org/10.1155/2020/2836236.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2818–2826.

W. Wang et al., “Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction Without Convolutions,” presented at the Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 568–578.

H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jegou, “Training data-efficient image transformers & distillation through attention,” in Proceedings of the 38th International Conference on Machine Learning, PMLR, Jul. 2021, pp. 10347–10357.

Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks,” arXiv.org. Accessed: Mar. 08, 2023. [Online]. Available: https://arxiv.org/abs/1910.03151v4

S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “CBAM: Convolutional Block Attention Module,” presented at the Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 3–19. [Online]. Available: https://openaccess.thecvf.com/content_ECCV_2018/html/Sanghyun_Woo_Convolutional_Block_Attention_ECCV_2018_paper.html

Q. Hou, D. Zhou, and J. Feng, “Coordinate Attention for Efficient Mobile Network Design,” presented at the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 13713–13722. Accessed: Mar. 11, 2023. [Online]. Available: https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Coordinate_Attention_for_Efficient_Mobile_Network_Design_CVPR_2021_paper.html