[1] SHENG B, CHEN X S, LI T Y, et al. An overview of artificial intelligence in diabetic retinopathy and other ocular diseases[J]. Frontiers in Public Health, 2022, 10:971943. [2] ASSI L, CHAMSEDDINE F, IBRAHIM P, et al. A global assessment of eye health and quality of life:a systematic review of systematic reviews[J]. JAMA Ophthalmology, 2021, 139(5):526-541. [3] 陈彬彬,楼丽霞,叶娟. 中国眼病疾病负担现状及三十年变化趋势[J]. 浙江大学学报(医学版), 2021, 50(4):420-428. CHEN B B, LOU L X, YE J. Eye diseases burden in China in the past 30 years[J]. Journal of Zhejiang University(Medical Sciences), 2021, 50(4):420-428. (in Chinese) [4] SENGAR N, JOSHI R C, DUTTA M K, et al. EyeDeep-Net:a multi-class diagnosis of retinal diseases using deep neural network[J]. Neural Computing and Applications, 2023, 35(14):10551-10571. [5] TSUNEKI M. Deep learning models in medical image analysis[J]. Journal of Oral Biosciences, 2022, 64(3):312-320. [6] CHEN X X, WANG X M, ZHANG K, et al. Recent advances and clinical applications of deep learning in medical image analysis[J]. Medical Image Analysis, 2022, 79:102444. [7] KUMAR Y, GUPTA S. Deep transfer learning approaches to predict glaucoma, cataract, choroidal neovascularization, diabetic macular edema, DRUSEN and healthy eyes:an experimental review[J]. Archives of Computational Methods in Engineering, 2023, 30(1):521-541. [8] BUTT M M, ISKANDAR D N F A, ABDELHAMID S E, et al. Diabetic retinopathy detection from fundus images of the eye using hybrid deep learning features[J]. Diagnostics, 2022, 12(7):1607. [9] BERNARDES R, SERRANHO P, LOBO C. Digital ocular fundus imaging:a review[J]. Ophthalmologica, 2011, 226(4):161-181. [10] 伍秀玭, 李珏炜, 高万荣. 眼底OCT图像的公共数据获取及其分析算法[J]. 激光与光电子学进展, 2023, 60(10):29-41. WU X P, LI J W, GAO W R. Public data acquisition of optical coherence tomography images of fundus and its analysis algorithms[J]. Laser & Optoelectronics Progress, 2023, 60(10):29-41.(in Chinese) [11] MONISHA B T, DIVYA C, MUTHUKUMARAN N. Computerized diagnosis of diabetic retinopathy based on deep learning techniques[C]//Proceedings of International Conference on Applied Artificial Intelligence and Computing. Washington D. C., USA:IEEE Press, 2022:346-358. [12] SINGH L K, POOJA, GARG H, et al. Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets[J]. Evolving Systems, 2022, 13(6):807-836. [13] HE J J, LI C, YE J, et al. Multi-label ocular disease classification with a dense correlation deep neural network[J]. Biomedical Signal Processing and Control, 2021, 63:102167. [14] LIN D R, XIONG J H, LIU C X, et al. Application of comprehensive artificial intelligence retinal expert system:a national real-world evidence study[J]. The Lancet Digital Health, 2021,3(8):486-495. [15] ATWANY M Z, SAHYOUN A H, YAQUB M. Deep learning techniques for diabetic retinopathy classification:a survey[J]. IEEE Access, 2022, 10:28642-28655. [16] SARKI R, AHMED K, WANG H, et al. Convolutional neural network for multi-class classification of diabetic eye disease[J]. ICST Transactions on Scalable Information Systems, 2018,18(1):172436. [17] JIN K, YAN Y, CHEN M L, et al. Multimodal deep learning with feature level fusion for identification of choroidal neovascularization activity in age-related macular degeneration[J]. Acta Ophthalmologica, 2022, 100(2):512-520. [18] TING D S W, PASQUALE L R, PENG L, et al. Artificial intelligence and deep learning in ophthalmology[J]. The British Journal of Ophthalmology, 2019, 103(2):167-175. [19] ZHENG C, XIE X L, ZHOU K, et al. Assessment of generative adversarial networks model for synthetic optical coherence tomography images of retinal disorders[J]. Translational Vision Science & Technology, 2020, 9(2):29. [20] CAO L C, LI H Q, ZHANG Y J, et al. Hierarchical method for cataract grading based on retinal images using improved Haar wavelet[J]. Information Fusion, 2020, 53:196-208. [21] RAMASAMY L K, PADINJAPPURATHU S G, KADRY S, et al. Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier[J]. Peerj Computer Science, 2021, 7:e456. [22] SHANG H, LI J Z, JIAO T Y, et al. Differentiation of lung metastases originated from different primary tumors using radiomics features based on CT imaging[J]. Academic Radiology, 2023, 30(1):40-46. [23] MOMENI POUR A, SEYEDARABI H, ABBASI JAHROMI S H, et al. Automatic detection and monitoring of diabetic retinopathy using efficient convolutional neural networks and contrast limited adaptive histogram equalization[J]. IEEE Access, 2020,8:136668-136673. [24] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA:IEEE Press,2016:1-9. [25] ALZAMILY J Y I, ARIFFIN S B, ABU-NASER S S. Classification of encrypted images using deep learning-ResNet50[J]. Journal of Theoretical and Applied Information Technology, 2022, 100(21):6610-6620. [26] RONNEBERGER O, FISCHER P, BROX T. U-Net:convolutional networks for biomedical image segmentation[M].Berlin, Germany:Springer,2015. [27] XIE S N, GIRSHICK R, DOLLAR P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA:IEEE Press, 2017:325-336. [28] ZHANG H, WU C R, ZHANG Z Y, et al. ResNest:split-attention networks[EB/OL].[2023-08-01].https://hangzhang.org/files/resnest.pdf. [29] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA:IEEE Press, 2016:338-347. [30] BOU NASSIF A, SHAHIN I, ATTILI I, et al. Speech recognition using deep neural networks:a systematic review[J]. IEEE Access, 2019, 7:19143-19165. [31] POLAT G, ERGENC I, KANI H T, et al. Class distance weighted cross-entropy loss for ulcerative colitis severity estimation[C]//Proceedings of Annual Conference on Medical Image Understanding and Analysis. Berlin, Germany:Springer, 2022:157-171. [32] VEIT A, WILBER M, BELONGIE S. Residual networks behave like ensembles of relatively shallow networks[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. New York, USA:ACM Press, 2016:550-558. [33] ASAOKA R, MURATA H, HIRASAWA K, et al. Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images[J]. American Journal of Ophthalmology, 2019, 198:136-145. [34] XU X L, CHEN W, SUN Y F. Over-sampling algorithm for imbalanced data classification[J]. Journal of Systems Engineering and Electronics, 2019, 30(6):1182-1191. [35] YAO K X, LIANG J Y, LIANG J Q, et al. Multi-view graph convolutional networks with attention mechanism[J]. Artificial Intelligence, 2022, 307:103708. [36] MASCARENHAS S, AGARWAL M. A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification[C]//Proceedings of International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications. Washington D. C.,USA:IEEE Press, 2021:579-588. [37] VIJAYAN T, SANGEETHA M, KARTHIK B. Efficient analysis of diabetic retinopathy on retinal fundus images using deep learning techniques with inception v3 architecture[J]. Journal of Green Engineering,2020, 10(10):9615-9625. [38] DU G T, CAO X, LIANG J M, et al. Medical image segmentation based on U-Net:a review[J]. Journal of Imaging Science and Technology, 2020, 64(2):1-12. [39] OKTAY O, SCHLEMPER J, FOLGOC L L, et al. Attention U-Net:learning where to look for the pancreas[EB/OL].[2023-08-01]. https://arxiv.org/pdf/1804.03999v2.pdf. [40] MUKHERJEE N, SENGUPTA S. Comparing deep feature extraction strategies for diabetic retinopathy stage classification from fundus images[J]. Arabian Journal for Science and Engineering, 2023, 48(8):10335-10354. |