14. Jinfeng G., Qummar S., Junming Z. [et al.]. Ensemble Framework of Deep CNNs for Diabetic Retinopathy Detection // Hindawi Computational Intelligence and Neuroscience. 2020. Vol. 2020. P. 1-11. doi:10.1155/2020/8864698
15. Mushtaq G., Siddiqui F. Detection of diabetic retinopathy using deep learning methodology // IOP Conference Series: Materials Science and Engineering. 2021. Vol. 1070, № 012049. P. 1-13. doi:10.1088/1757-899X/1070/1/012049
16. Lin G.-M., Chen M.-J., Yeh C.-H. [et al.]. Transforming Retinal Photographs to En-tropy Images in Deep Learning to Improve Automated Detection for Diabetic Reti-nopathy // Hindawi Journal of Ophthalmology. 2018. Vol. 2018. P. 1-6. doi:10.1155/2018/2159702
17. Nneji G. U., Cai J., Deng J. [et al.]. Identification of Diabetic Retinopathy Using Weighted Fusion Deep Learning Based on Dual-Channel Fundus Scans // Diagnostics. 2022. Vol. 12, № 2. P. 1-19. doi:10.3390/diagnostics12020540
18. Liu H., Yue K., Cheng S. [et al.]. Hybrid Model Structure for Diabetic Retinopathy Classification // Hindawi Journal of Healthcare Engineering. 2020. Vol. 2020. P. 1-9. doi:10.1155/2020/8840174
19. Kaushik H., Singh D., Kaur M. [et al.]. Diabetic Retinopathy Diagnosis From Fundus Images Using Stacked Generalization of Deep Models // IEEE Access. 2021. Vol. 9. P. 108276-108292. doi:10.1109/ACCESS.2021.3101142
20. Rio J. M. N. do, Nderitu P., Bergeles C. [et al.]. Evaluating a Deep Learning Diabetic Retinopathy Grading System Developed on Mydriatic Retinal Images When Applied to Non-Mydriatic Community Screening // Journal of Clinical Medicine. 2022. Vol. 11, № 3. P. 1-11. doi:10.3390/jcm11030614
21. Torre J. de la, Valls A., Puig D. A deep learning interpretable classifier for diabetic retinopathy disease grading // Neurocomputing. 2020. Vol. 396. P. 465-476. doi: 10.1016/j .neucom.2018.07.102
22. Lo J.-E., Kang E. Y.-C., Chen Y.-N. [et al.]. Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy // Hindawi Journal of Diabetes Research. 2021. Vol. 2021. P. 1-9. doi:10.1155/2021/2751695
23. Dai L., Wu L., Li H. [et al.]. A deep learning system for detecting diabetic retinopathy across the disease spectrum // Nature Communications. 2021. Vol. 12, № 3242. P. 1-11. doi:10.1038/s41467-021-23458-5
24. Norgaard M. F., Grauslund J. Automated Screening for Diabetic Retinopathy - A Systematic Review // Ophthalmic Research. 2018. Vol. 60, № 1. P. 1-9. doi: 10.1159/000486284
25. Baget-Bernaldiz M., Pedro R.-A., Santos-Blanco E. [et al.]. Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database // Diagnostics. 2021. Vol. 11, № 8. P. 1-11. doi:10.3390/ diagnostics11081385
26. Islam M. M., Yang H.-C., Poly T. N. [et al.]. Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and metaanalysis // Computer Methods and Programs in Biomedicine. 2020. Vol. 191, № 10. P. 1-16. doi:10.1016/j.cmpb.2020.105320
27. Voets M., Mollersen K., Bongo L. A. Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs // PLOS ONE. 2019. Vol. 14, № 6. P. 1-11. doi:10.1371/journal.pone.0217541
28. Shaban M., Ogur Z., Mahmoud A. [et al.]. A convolutional neural network for the screening and staging of diabetic retinopathy // PLOS ONE. 2020. Vol. 15, № 6. P. 1
13. doi:10.1371/journal.pone.0233514
29. Yaqoob M. K., Ali S. F., Bilal M. [et al.]. ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection // Sensors. 2021. Vol. 21, № 11. P. 1-14. doi:10.3390/s21113883
30. Mateen M., Malik T. S., Hayat S. [et al.]. Deep Learning Approach for Automatic Microaneurysms Detection // Sensors. 2022. Vol. 22, № 2. P. 1-14. doi: 10.3390/ s22020542
31. Sikder N., Masud M., Bairagi A. K. [et al.]. Severity Classification of Diabetic Reti-nopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images // Symmetry. 2021. Vol. 13, № 4. P. 1-26. doi:10.3390/sym13040670
32. Vora P., Shrestha S. Detecting Diabetic Retinopathy Using Embedded Computer Vision // Applied Sciences. 2020. Vol. 10, № 20. P. 1-10. doi:10.3390/app10207274
33. Chen P.-N., Lee C.-C., Liang C.-M. [et al.]. General deep learning model for detecting diabetic retinopathy // BMC Bioinformatics. 2021. Vol. 22, № 84. P. 1-14. doi:10.1186/s12859-021-04005-x
34. Pires R., Avila S., Wainer J. [et al.]. A data-driven approach to referable diabetic retinopathy detection // Artificial Intelligence In Medicine. 2019. Vol. 96. P. 93-106. doi:10.1016/j.artmed.2019.03.009
35. Adriman R., Muchtar K., Maulina N. Performance Evaluation of Binary Classification of Diabetic Retinopathy through Deep Learning Techniques using Texture Feature // Procedia Computer Science. 2021. Vol. 179. P. 88-94. doi:10.1016/j.procs.2020.12.012
36. Hacisoftaoglu R. E., Karakaya M., Sallam A. B. Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems // Pattern Recognition Letters. 2020. Vol. 135, № 4. P. 409-417. doi:10.1016/j.patrec.2020.04.009
37. Wan S., Liang Y., Zhang Y. Deep convolutional neural networks for diabetic reti-nopathy detection by image classification // Computers and Electrical Engineering. 2018. Vol. 72. P. 274-282. doi:10.1016/j.compeleceng.2018.07.042
38. Majumder S., Kehtarnavaz N. Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy // IEEE Access. 2021. Vol. 9. P. 123220-123230. doi:10.1109/ACCESS.2021.3109240
39. Goel S., Gupta S., Panwar A. [et al.]. Deep Learning Approach for Stages of Severity Classification in Diabetic Retinopathy Using Color Fundus Retinal Images // Hindawi Mathematical Problems in Engineering. 2021. Vol. 2021. P. 1-8. doi:10.1155/2021/ 7627566
40. Nazir T., Irtaza A., Javed A. [et al.]. Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning // Applied Sciences. 2020. Vol. 10, № 18. P. 1-21. doi:10.3390/app10186185
41. Nazir T., Nawaz M., Rashid J. [et al.]. Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model // Sensors. 2021. Vol. 21, № 16. P. 1-18. doi:10.3390/s21165283
42. Li T., Gao Y., Wang K. [et al.]. Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening // Information Sciences. 2019. Vol. 501, № 13. P. 511-522. doi:10.1016/j.ins.2019.06.011
43. Vives-Boix V., Ruiz-Fernandez D. Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity // Computer Methods and Programs in Biomedicine. 2021. Vol. 206, № 6. P. 1-8. doi:10.1016/j.cmpb.2021.106094
44. Zhang W., Zhong J., Yang S. [et al.]. Automated identification and grading system of diabetic retinopathy using deep neural networks // Knowledge-Based Systems. 2019. Vol. 175. P. 12-25. doi:10.1016/j.knosys.2019.03.016
45. Alyoubi W. L., Abulkhair M. F., Shalash W. M. Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning // Sensors. 2021. Vol. 21, № 11. P. 1-22. doi:10.3390/s21113704
46. Zeng X., Chen H., Luo Y., Ye W. Automated Diabetic Retinopathy Detection Based on Binocular Siamese-Like Convolutional Neural Network // IEEE Access. 2019. Vol. 7. P. 30744-30753. doi:10.1109/ACCESS.2019.2903171
47. Liu Y.-P., Li Z., Xu C. [et al.]. Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network // Artificial Intelligence In Medicine. 2019. Vol. 99, № 3. P. 1-7. doi:10.1016/j.artmed.2019.07.002
48. Hsieh Y.-T., Chuang L.-M., Jiang Y.-D. [et al.]. Application of deep learning image assessment software VeriSee for diabetic retinopathy screening // Journal of the Formosan Medical Association. 2021. Vol. 120, № 1. P. 165-171. doi:10.1016/ jjfma.2020.03.024
49. Li Y.-H., Yeh N.-N., Chen S.-J., Chung Y.-C. Computer-Assisted Diagnosis for Dia-betic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Network // Hindawi Mobile Information Systems. 2019. Vol. 2019. P. 1-14. doi:10.1155/ 2019/6142839
50. Khan Z., Khan F. G., Khan A. [et al.]. Diabetic Retinopathy Detection Using VGG- NIN a Deep Learning Architecture // IEEE Access. 2021. Vol. 9. P. 61408-61416. doi:10.1109/ACCESS.2021.3074422
References
1. Dedov I.I., Shestakova M.V., Vikulova O.K. [et al.]. Atlas of the register of diabetes mellitus of the Russian Federation. Status 2018. Sakharnyy diabet = Diabetes mellitus. 2019;22(2S):4-61. (In Russ.). doi:10.14341/DM12208
2. Chukhraev A.M., Khodzhaev N.S., Kechin E.V. Analysis of the structure of telemedicine consultations on the profile "Ophthalmology" in the Russian Federation. Zdra- vookhranenie Rossiyskoy Federatsii = Health of the Russian Federation. 2020;64(1):22-28. (In Russ.). doi:10.18821/0044-197X-2019-64-1-22-28
3. Chernykh V.V., Khodzhaev N.S., Shakhov V.G. Methodology of development of telemedicine and information systems of FSAU "MNTC "Eye Microsurgery" named after Academician S.N. Fedorov" of the Ministry of Health of Russia on the example of the Novosibirsk branch. Oftal'mokhirurgiya = Ophthalmosurgery. 2018;(1):84-90. (In Russ.). doi:10.25276/0235-4160-2018-1-84-90
4. Dobrov E.R. Application of big data in teleophthalmology. Inzhenernyy vestnik Dona = Engineering Bulletin of the Don. 2021;(7):138-157. (In Russ.)
5. Neroev V.V., Bragin A.A., Zaytseva O.V. Development of a prototype service for the diagnosis of diabetic retinopathy from fundus images using artificial intelligence methods. Natsional'noe zdravookhranenie = National Healthcare. 2021;2(2):64-72. (In Russ.). doi:10.47093/2713-069X.2021.2.2.64-72
6. Samanta A., Saha A., Satapathy S.C. [et al.]. Automated detection of diabetic reti-nopathy using convolutional neural networks on a small dataset. Pattern Recognition Letters. 2020;135:293-298. doi:10.1016/j.patrec.2020.04.026
7. Ayala A., Figueroa T.O., Fernandes B., Cruz F. Diabetic Retinopathy Improved De-tection Using Deep Learning. Applied Sciences. 2021;11(24): 1-11.
doi:10.3390/app112411970
8. Bora A., Balasubramanian S., Babenko B. [et al.]. Predicting the risk of developing diabetic retinopathy using deep learning. The Lancet Digital Health. 2021;3(1): 10-19. doi:10.1016/S2589-7500(20)30250-8
9. Sugeno A., Ishikawa Y., Ohshima T., Muramatsu R. Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning. Computers in Biology and Medicine. 2021;137(14):1-9.
doi:10.1016/j.compbiomed.2021.104795
10. Biyani R.S., Patre B.M. Algorithms for red lesion detection in Diabetic Retinopathy: A review. Biomedicine & Pharmacotherapy. 2018;107(4):681-688.
doi:10.1016/j.biopha.2018.07.175
11. Tsai C.-Y., Chen C.-T., Chen G.-A. [et al.]. Necessity of Local Modification for Deep Learning Algorithms to Predict Diabetic Retinopathy. International Journal of Environmental Research and Public Health. 2022;19(3):1-12.
doi:10.3390/ijerph19031204
12. Zia F., Irum I., Qadri N.N. [et al.]. A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification. Computers, Materials & Continua. 2022;70(2):2261-2276. doi:10.32604/cmc.2022.017820
13. Zago G.T., Andreao R.V., Dorizzi B. [et al.]. Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Computers in Biology and Medicine. 2020;116:1-12. doi:10.1016/j.compbiomed.2019.103537
14. Jinfeng G., Qummar S., Junming Z. [et al.]. Ensemble Framework of Deep CNNs for Diabetic Retinopathy Detection. Hindawi Computational Intelligence and Neuroscience. 2020;2020:1-11. doi:10.1155/2020/8864698
15. Mushtaq G., Siddiqui F. Detection of diabetic retinopathy using deep learning methodology. IOP Conference Series: Materials Science and Engineering.
2021 ;1070(012049):1-13. doi:10.1088/1757-899X/1070/1/012049
16. Lin G.-M., Chen M.-J., Yeh C.-H. [et al.]. Transforming Retinal Photographs to En-tropy Images in Deep Learning to Improve Automated Detection for Diabetic Reti-nopathy. Hindawi Journal of Ophthalmology. 2018;2018:1-6.
doi:10.1155/2018/2159702
17. Nneji G.U., Cai J., Deng J. [et al.]. Identification of Diabetic Retinopathy Using Weighted Fusion Deep Learning Based on Dual-Channel Fundus Scans. Diagnostics. 2022;12(2):1-19. doi:10.3390/diagnostics12020540
18. Liu H., Yue K., Cheng S. [et al.]. Hybrid Model Structure for Diabetic Retinopathy Classification. Hindawi Journal of Healthcare Engineering. 2020;2020:1-9. doi:10.1155/2020/8840174
19. Kaushik H., Singh D., Kaur M. [et al.]. Diabetic Retinopathy Diagnosis From Fundus Images Using Stacked Generalization of Deep Models. IEEE Access. 2021;9:108276- 108292. doi:10.1109/ACCESS.2021.3101142
20. Rio J.M.N. do, Nderitu P., Bergeles C. [et al.]. Evaluating a Deep Learning Diabetic Retinopathy Grading System Developed on Mydriatic Retinal Images When Applied to Non-Mydriatic Community Screening. Journal of Clinical Medicine. 2022;11(3):1- 11. doi:10.3390/jcm11030614
21. Torre J. de la, Valls A., Puig D. A deep learning interpretable classifier for diabetic retinopathy disease grading. Neurocomputing. 2020;396:465-476.
doi: 10.1016/j.neucom.2018.07.102
22. Lo J.-E., Kang E. Y.-C., Chen Y.-N. [et al.]. Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy. Hindawi Journal of Diabetes Research. 2021;2021:1-9. doi:10.1155/2021/2751695
23. Dai L., Wu L., Li H. [et al.]. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature Communications. 2021;12(3242):1-11. doi:10.1038/s41467-021-23458-5
24. Nergaard M.F., Grauslund J. Automated Screening for Diabetic Retinopathy - A Systematic Review. Ophthalmic Research. 2018;60(1):1-9. doi:10.1159/000486284
25. Baget-Bernaldiz M., Pedro R.-A., Santos-Blanco E. [et al.]. Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database. Diagnostics. 2021;11(8): 1-11.
doi:10.33 90/diagnostics11081385
26. Islam M.M., Yang H.-C., Poly T. N. [et al.]. Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and metaanalysis. Computer Methods and Programs in Biomedicine. 2020;191(10):1-16. doi:10.1016/j.cmpb.2020.105320
27. Voets M., Mellersen K., Bongo L.A. Reproduction study using public data of: Devel opment and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PLOS ONE. 2019;14(6): 1-11.
doi:10.1371/journal.pone.0217541
28. Shaban M., Ogur Z., Mahmoud A. [et al.]. A convolutional neural network for the screening and staging of diabetic retinopathy. PLOS ONE. 2020;15(6):1-13. doi:10.1371/journal.pone.0233514
29. Yaqoob M.K., Ali S.F., Bilal M. [et al.]. ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection. Sensors. 2021;21(11):1-14. doi:10.3390/s21113883
30. Mateen M., Malik T. S., Hayat S. [et al.]. Deep Learning Approach for Automatic Microaneurysms Detection. Sensors. 2022;22(2):1-14. doi:10.3390/s22020542
31. Sikder N., Masud M., Bairagi A.K. [et al.]. Severity Classification of Diabetic Reti-nopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images. Symmetry. 2021;13(4):1-26. doi:10.3390/sym13040670
32. Vora P., Shrestha S. Detecting Diabetic Retinopathy Using Embedded Computer Vision. Applied Sciences. 2020;10(20):1-10. doi:10.3390/app10207274
33. Chen P.-N., Lee C.-C., Liang C.-M. [et al.]. General deep learning model for detecting diabetic retinopathy. BMC Bioinformatics. 2021;22(84):1-14. doi: 10.1186/s12859- 021-04005-x
34. Pires R., Avila S., Wainer J. [et al.]. A data-driven approach to referable diabetic retinopathy detection. Artificial Intelligence In Medicine. 2019;96:93-106. doi:10.1016/j.artmed.2019.03.009
35. Adriman R., Muchtar K., Maulina N. Performance Evaluation of Binary Classification of Diabetic Retinopathy through Deep Learning Techniques using Texture Feature. Procedia Computer Science. 2021;179:88-94. doi:10.1016/j.procs.2020.12.012
36. Hacisoftaoglu R.E., Karakaya M., Sallam A.B. Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems. Pattern Recognition Letters. 2020;135(4):409-417. doi:10.1016/j.patrec.2020.04.009
37. Wan S., Liang Y., Zhang Y. Deep convolutional neural networks for diabetic reti-nopathy detection by image classification. Computers and Electrical Engineering. 2018;72:274-282. doi:10.1016/j .compeleceng.2018.07.042
38. Majumder S., Kehtarnavaz N. Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy. IEEE Access. 2021;9:123220-123230. doi:10.1109/ACCESS.2021.3109240
39. Goel S., Gupta S., Panwar A. [et al.]. Deep Learning Approach for Stages of Severity Classification in Diabetic Retinopathy Using Color Fundus Retinal Images. Hindawi Mathematical Problems in Engineering. 2021;2021:1-8. doi:10.1155/2021/7627566
40. Nazir T., Irtaza A., Javed A. [et al.]. Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning. Applied Sciences. 2020;10(18):1-21. doi:10.3390/app10186185
41. Nazir T., Nawaz M., Rashid J. [et al.]. Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model. Sensors. 2021;21(16): 1-18. doi:10.3390/s21165283
42. Li T., Gao Y., Wang K. [et al.]. Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Information Sciences. 2019;501(13):511-522.
doi:10.1016/j.ins.2019.06.011
43. Vives-Boix V., Ruiz-Fernandez D. Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity. Computer Methods and Programs in Biomedicine. 2021;206(6):1-8. doi:10.1016/j.cmpb.2021.106094
44. Zhang W., Zhong J., Yang S. [et al.]. Automated identification and grading system of diabetic retinopathy using deep neural networks. Knowledge-Based Systems. 2019;175:12-25. doi:10.1016/j.knosys.2019.03.016
45. Alyoubi W.L., Abulkhair M.F., Shalash W.M. Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning. Sensors. 2021;21(11): 1-22. doi:10.3390/s21113704
46. Zeng X., Chen H., Luo Y., Ye W. Automated Diabetic Retinopathy Detection Based on Binocular Siamese-Like Convolutional Neural Network. IEEE Access. 2019;7:30744-30753. doi:10.1109/ACCESS.2019.2903171
47. Liu Y.-P., Li Z., Xu C. [et al.]. Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network. Artificial Intelligence In Medicine. 2019;99(3):1-7. doi:10.1016/j.artmed.2019.07.002
48. Hsieh Y.-T., Chuang L.-M., Jiang Y.-D. [et al.]. Application of deep learning image assessment software VeriSee for diabetic retinopathy screening. Journal of the Formosan Medical Association. 2021 ;120(1): 165-171. doi:10.1016/j.jfma.2020.03.024
49. Li Y.-H., Yeh N.-N., Chen S.-J., Chung Y.-C. Computer-Assisted Diagnosis for Dia
betic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Net-work. Hindawi Mobile Information Systems. 2019;2019:1-14.
doi:10.1155/2019/6142839
50. Khan Z., Khan F.G., Khan A. [et al.]. Diabetic Retinopathy Detection Using VGG- NIN a Deep Learning Architecture. IEEE Access. 2021;9:61408-61416. doi:10.1109/ACCESS.2021.3074422