Current - Issue
Year 2026 · Volume 5 · Issue 2
Original Article
A Transparent Hybrid Quantum-Classical Framework for Automated Multi-Class Diabetic Retinopathy Grading
Bilal Babayigit1
Mustafa S. M. Alazzawi2
Ahmed alkhateb3
1 2 Department of Computer Engineering, Faculty of Engineering, Erciyes University Kayseri, Türkiye. 3 Ibn Sina University for Medical Sciences, Medical College, Baghdad, Iraq.
Published Online: May-August 2026
Pages: 1066-1078
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502115
References
1. M. R. Rooney, J. H. He, P. Salpea, I. Genitsaridi, D. J. Magliano, E. J. Boyko, and E. Selvin, “Global and regional prediabetes prevalence: Updates for 2024 and projections for 2050,” Diabetes Care, vol. 48, no. 11, pp. e142–e144, 2025.
2. H. Seo, S. J. Park, and M. Song, “Diabetic retinopathy: Mechanisms, current therapies, and emerging strategies,” Cells, vol. 14, no. 5, Art. no. 376, Mar. 2025, doi: 10.3390/cells14050376.
3. A. M. Hendrick, M. V. Gibson, and A. Kulshreshtha, “Diabetic retinopathy: Review of epidemiology and clinical management,” Primary Care: Clinics in Office Practice, vol. 47, no. 1, pp. 145–159, Mar. 2020.
4. M. D. Abramoff, P. T. Lavin, M. Birch, N. Shah, and J. C. Folk, “Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices,” npj Digital Medicine, vol. 1, no. 1, pp. 1–8, Aug. 2020.
5. C. P. Wilkinson, F. L. Ferris, R. E. Klein et al., “Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales,” Ophthalmology, vol. 110, no. 9, pp. 1677–1682, Sep. 2003.
6. P. Romero-Aroca, M. Baget-Bernaldiz, and A. Pareja-Rios, “Current status of automated diabetic retinopathy screening with fundus photography,” Journal of Diabetes Research, vol. 2020, pp. 1–11, 2020.
7. A. D. Fleming, S. Philip, K. A. Goatman, J. A. Olson, and P. F. Sharp, “Automated assessment of diabetic retinal image quality based on clarity and field definition,” Investigative Ophthalmology & Visual Science, vol. 47, no. 3, pp. 1120–1125, Mar. 2006.
8. R. Gargeya and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology, vol. 124, no. 7, pp. 962–969, Jul. 2017.
9. World Health Organization, World Report on Vision. Geneva, Switzerland: WHO, 2020. [Online]. Available: https://www.who.int/publications/i/item/9789241516570
10. A. Abou Taha, S. Dinesen, A. S. Vergmann, and J. Grauslund, “Present and future screening programs for diabetic retinopathy: A narrative review,” International Journal of Retina and Vitreous, vol. 10, no. 1, Art. no. 14, 2024.
11. G. Quellec, K. Charrière, Y. Boudi, B. Cochener, and M. Lamard, “Deep image mining for diabetic retinopathy screening,” Medical Image Analysis, vol. 64, Art. no. 101750, Feb. 2020.
12. G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, Dec. 2017.
13. J. Ker, L. Wang, J. Rao, and T. Lim, “Deep learning applications in medical image analysis,” IEEE Access, vol. 6, pp. 9375–9389, 2018.
14. G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 4700–4708.
15. H. Pratt, F. Coenen, D. M. Broadbent, S. P. Harding, and Y. Zheng, “Convolutional neural networks for diabetic retinopathy,” Procedia Computer Science, vol. 90, pp. 200–205, 2016.
16. A. Agrawal, N. Mueller, and P. Gupta, “Ensemble deep learning for diabetic retinopathy severity classification,” Computers in Biology and Medicine, vol. 135, Art. no. 104548, Aug. 2021.
17. Z. C. Lipton, “The mythos of model interpretability,” Queue, vol. 16, no. 3, pp. 31–57, Jun. 2018.
18. U.S. Food and Drug Administration, Artificial Intelligence and Machine Learning in Software as a Medical Device. Silver Spring, MD, USA: FDA, 2021. [Online]. Available: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device
19. E. Tjoa and C. Guan, “A survey on explainable artificial intelligence: Toward medical XAI,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 11, pp. 4793–4813, Nov. 2021.
20. S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proc. Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA, 2017, pp. 4765–4774.
21. L. S. Shapley, “A value for n-person games,” in Contributions to the Theory of Games, H. W. Kuhn and A. W. Tucker, Eds. Princeton, NJ, USA: Princeton Univ. Press, 1953, pp. 307–317.
22. M. Cerezo et al., “Variational quantum algorithms,” Nature Reviews Physics, vol. 3, no. 9, pp. 625–644, Sep. 2021.
23. S. Sim, P. D. Johnson, and A. Aspuru-Guzik, “Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms,” Advanced Quantum Technologies, vol. 2, no. 12, Art. no. 1900070, Dec. 2019.
24. J. Preskill, “Quantum computing in the NISQ era and beyond,” Quantum, vol. 2, Art. no. 79, Aug. 2018.
2. H. Seo, S. J. Park, and M. Song, “Diabetic retinopathy: Mechanisms, current therapies, and emerging strategies,” Cells, vol. 14, no. 5, Art. no. 376, Mar. 2025, doi: 10.3390/cells14050376.
3. A. M. Hendrick, M. V. Gibson, and A. Kulshreshtha, “Diabetic retinopathy: Review of epidemiology and clinical management,” Primary Care: Clinics in Office Practice, vol. 47, no. 1, pp. 145–159, Mar. 2020.
4. M. D. Abramoff, P. T. Lavin, M. Birch, N. Shah, and J. C. Folk, “Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices,” npj Digital Medicine, vol. 1, no. 1, pp. 1–8, Aug. 2020.
5. C. P. Wilkinson, F. L. Ferris, R. E. Klein et al., “Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales,” Ophthalmology, vol. 110, no. 9, pp. 1677–1682, Sep. 2003.
6. P. Romero-Aroca, M. Baget-Bernaldiz, and A. Pareja-Rios, “Current status of automated diabetic retinopathy screening with fundus photography,” Journal of Diabetes Research, vol. 2020, pp. 1–11, 2020.
7. A. D. Fleming, S. Philip, K. A. Goatman, J. A. Olson, and P. F. Sharp, “Automated assessment of diabetic retinal image quality based on clarity and field definition,” Investigative Ophthalmology & Visual Science, vol. 47, no. 3, pp. 1120–1125, Mar. 2006.
8. R. Gargeya and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology, vol. 124, no. 7, pp. 962–969, Jul. 2017.
9. World Health Organization, World Report on Vision. Geneva, Switzerland: WHO, 2020. [Online]. Available: https://www.who.int/publications/i/item/9789241516570
10. A. Abou Taha, S. Dinesen, A. S. Vergmann, and J. Grauslund, “Present and future screening programs for diabetic retinopathy: A narrative review,” International Journal of Retina and Vitreous, vol. 10, no. 1, Art. no. 14, 2024.
11. G. Quellec, K. Charrière, Y. Boudi, B. Cochener, and M. Lamard, “Deep image mining for diabetic retinopathy screening,” Medical Image Analysis, vol. 64, Art. no. 101750, Feb. 2020.
12. G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, Dec. 2017.
13. J. Ker, L. Wang, J. Rao, and T. Lim, “Deep learning applications in medical image analysis,” IEEE Access, vol. 6, pp. 9375–9389, 2018.
14. G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 4700–4708.
15. H. Pratt, F. Coenen, D. M. Broadbent, S. P. Harding, and Y. Zheng, “Convolutional neural networks for diabetic retinopathy,” Procedia Computer Science, vol. 90, pp. 200–205, 2016.
16. A. Agrawal, N. Mueller, and P. Gupta, “Ensemble deep learning for diabetic retinopathy severity classification,” Computers in Biology and Medicine, vol. 135, Art. no. 104548, Aug. 2021.
17. Z. C. Lipton, “The mythos of model interpretability,” Queue, vol. 16, no. 3, pp. 31–57, Jun. 2018.
18. U.S. Food and Drug Administration, Artificial Intelligence and Machine Learning in Software as a Medical Device. Silver Spring, MD, USA: FDA, 2021. [Online]. Available: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device
19. E. Tjoa and C. Guan, “A survey on explainable artificial intelligence: Toward medical XAI,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 11, pp. 4793–4813, Nov. 2021.
20. S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proc. Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA, 2017, pp. 4765–4774.
21. L. S. Shapley, “A value for n-person games,” in Contributions to the Theory of Games, H. W. Kuhn and A. W. Tucker, Eds. Princeton, NJ, USA: Princeton Univ. Press, 1953, pp. 307–317.
22. M. Cerezo et al., “Variational quantum algorithms,” Nature Reviews Physics, vol. 3, no. 9, pp. 625–644, Sep. 2021.
23. S. Sim, P. D. Johnson, and A. Aspuru-Guzik, “Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms,” Advanced Quantum Technologies, vol. 2, no. 12, Art. no. 1900070, Dec. 2019.
24. J. Preskill, “Quantum computing in the NISQ era and beyond,” Quantum, vol. 2, Art. no. 79, Aug. 2018.
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