Diabetic Retinopathy Diagnosis with Deep Learning

Article Sidebar

pdf
Published: 2026-02-25
DOI: https://doi.org/10.15662/IJRPETM.2026.0902004
Keywords:
Diabetic Retinopathy (DR), Artificial Intelligence (AI), Deep Learning (DL), Convolutional Neural Networks (CNN), Vision Transformers (VITS), Ensemble Learning, Normalization, High Accuracy, Microaneurysms, Hemorrhages

Main Article Content

N. Akshaya, Y. Balaji, J. Chennarao, P. Sathwik
UG student, Department of Computer Science and Engineering, Holy Mary Institute of Technology & Science, Telangana, India
Dr. Prasad Dharnasi
UG student, Department of Computer Science and Engineering, Holy Mary Institute of Technology & Science, Telangana, India

Abstract

Diabetic retinopathy (DR) is one of the leading causes of vision impairment and blindness among diabetic patients worldwide. Early detection and timely treatment are essential to prevent severe vision loss; however, manual screening of retinal fundus images is time-consuming, costly, and prone to human error. Recent advances in deep learning have enabled the development of automated systems for accurate and efficient diagnosis of diabetic retinopathy. a deep learning–based approach is proposed for the detection and classification of diabetic retinopathy using retinal fundus images. Convolutional Neural Networks (CNNs) are employed to automatically extract relevant features such as microaneurysms, hemorrhages, and exudates without the need for handcrafted feature extraction. The model is trained and evaluated on publicly available retinal image datasets after preprocessing steps including image resizing, normalization, and contrast enhancement. Experimental results demonstrate that the proposed deep learning model achieves high accuracy, sensitivity, and specificity in classifying different stages of diabetic retinopathy 

Article Details

Issue

Vol. 9 No. 2 (2026): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Diabetic Retinopathy Diagnosis with Deep Learning. (2026). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(2), 506-512. https://doi.org/10.15662/IJRPETM.2026.0902004

References

1. Amitha, K., Ram Manohar Reddy, M., Yashwanth, K., Shylaja, K., Rahul Reddy, M., Srinu, B., & Dharnasi, P. (2026). AI empowered security monitoring system with the help of deployed ML models. International Journal of Computer Technology and Electronics Communication (IJCTEC), 9(1), 69–73.

2. Itoo, S., Khan, A. A., Ahmad, M., & Idrisi, M. J. (2023). A secure and privacy-preserving lightweight authentication and key exchange algorithm for smart agriculture monitoring system. IEEE Access, 11, 56875-56890.

3. Vaidya, S., Shah, N., Shah, N., & Shankarmani, R. (2020, May). Real-time object detection for visually challenged people. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 311-316). IEEE.

4. Gogada, S., Gopichand, K., Reddy, K. C., Keerthana, G., Nithish Kumar, M., Shivalingam, N., & Dharnasi, P. (2026). Cloud computing/deep learning customer churn prediction for SaaS platforms. International Journal of Computer Technology and Electronics Communication (IJCTEC), 9(1), 74–78.

5. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.

6. Akula, A., Budha, G., Bingi, G., Chanda, U., Borra, A. R., Yadav, D. B., & Saravanan, M. (2026). Emotion recognition from facial expressions using CNNs. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(1), 120–125.

7. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.

8. Kiran, A., & Kumar, S. A methodology and an empirical analysis to determine the most suitable synthetic data generator. IEEE Access 12, 12209–12228 (2024).

9. Varshini, M., Chandrapathi, M., Manirekha, G., Balaraju, M., Afraz, M., Sarvanan, M., & Dharnasi, P. (2026). ATM access using card scanner and face recognition with AIML. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(1), 113–118.

10. Thirumal, L., & Umasankar, P. (2026). Precision muscle segmentation and classification for knee osteoarthritis with dual attention networks and GAO-optimized CNN. Biomedical Signal Processing and Control, 111, 108244

11. Feroz, A., Pranay, D., Srikar Sai Raj, B., Harsha Vardhan, C., Rohith Raja, B., Nirmala, B., & Dharnasi, P. (2026). Blockchain and machine learning combined secured voting system. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(1), 119–124.

12. Ananth, S., Radha, K., & Raju, S. (2024). Animal Detection In Farms Using OpenCV In Deep Learning. Advances in Science and Technology Research Journal, 18(1), 1.

13. Inbavalli, M., & Arasu, T. (2015). Efficient Analysis of Frequent Item Set Association Rule Mining Methods. International Journal of Scientific & Engineering Research, 6(4).

14. Tirupalli, S. R., Munduri, S. K., Sangaraju, V., Yeruva, S. D., Saravanan, M., & Dharnasi, P. (2026). Blockchain integration with cloud storage for secure and transparent file management. International Journal of Computer Technology and Electronics Communication (IJCTEC), 9(1), 79–86.

15. Prasanna, D., & Santhosh, R. (2018). Time Orient Trust Based Hook Selection Algorithm for Efficient Location Protection in Wireless Sensor Networks Using Frequency Measures. International Journal of Engineering & Technology, 7(3.27), 331-335.

16. Chandu, S., Goutham, T., Badrinath, P., Prashanth Reddy, V., Yadav, D. B., & Dharnas, P. (2026). Biometric authentication using IoT devices powered by deep learning and encrypted verification. International Journal of Computer Technology and Electronics Communication (IJCTEC), 9(1), 87–92.

17. Aashiq Banu, S., Sucharita, M. S., Soundarya, Y. L., Nithya, L., Dhivya, R., & Rengarajan, A. (2020). Robust Image Encryption in Transform Domain Using Duo Chaotic Maps—A Secure Communication. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (pp. 271-281). Singapore: Springer Singapore.

18. Singh, K., Amrutha Varshini, G., Karthikeya, M., Manideep, G., Sarvanan, M., & Dharnasi, P. (2026). Automatic brand logo detection using deep learning. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(1), 126–130.

19. Neela Madheswari, A., Vijayakumar, R., Kannan, M., Umamaheswari, A., & Menaka, R. (2022). Text-to-speech synthesis of indian languages with prosody generation for blind persons. In IOT with Smart Systems: Proceedings of ICTIS 2022, Volume 2 (pp. 375-380). Singapore: Springer Nature Singapore.

20. Keerthana, L. M., Mounika, G., Abhinaya, K., Zakeer, M., Chowdary, K. M., Bhagyaraj, K., & Prasad, D. (2026). Floods and landslide prediction using machine learning. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(1), 125–129.

21. Gopinathan, V. R. (2025). Designing Cloud-Native Enterprise Systems by Modernizing Applications with Microservices and Kubernetes Platforms. International Journal of Research and Applied Innovations, 8(5), 13052-13063.

22. Kumar, A. S., Saravanan, M., Joshna, N., & Seshadri, G. (2019). Contingency analysis of fault and minimization of power system outage using fuzzy controller. International Journal of Innovative Technology and Exploring Engineering, 9(1), 4111-4115.

23. Dadigari, M., Appikatla, S., Gandhala, Y., Bollu, S., Macha, K., & Saravanan, M. (2026). Bitcoin price prediction with ML through blockchain technology. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(1), 130–136.

24. Chinthala, S., Erla, P. K., Dongari, A., Bantu, A., Chityala, S. G., & Saravanan, M. S. (2026). Food recognition and calorie estimation using machine learning. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(2), 480–488.

25. Dharnasi, P. (2025). A Multi-Domain AI Framework for Enterprise Agility Integrating Retail Analytics with SAP Modernization and Secure Financial Intelligence. International Journal of Humanities and Information Technology, 7(4), 61-66.

26. Vimal Raja, G. (2025). Context-Aware Demand Forecasting in Grocery Retail Using Generative AI: A Multivariate Approach Incorporating Weather, Local Events, and Consumer Behaviour. International Journal of Innovative Research in Science Engineering and Technology (Ijirset), 14(1), 743-746.

27. Chinthamalla, N., Anumula, G., Banja, N., Chelluboina, L., Dangeti, S., Jitendra, A., & Saravanan, M. (2026). IoT-based vehicle tracking with accident alert system. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(2), 486–494.

28. Nagamani, K., Laxmikala, K., Sreeram, K., Eshwar, K., Jitendra, A., & Dharnasi, P. (2026). Disaster management and earthquake prediction system using machine learning. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(2), 495–499.

29. Harish, M., & Selvaraj, S. K. (2023, August). Designing efficient streaming-data processing for intrusion avoidance and detection engines using entity selection and entity attribute approach. In AIP Conference Proceedings (Vol. 2790, No. 1, p. 020021). AIP Publishing LLC.

30. Prasad, E. D., Sahithi, B., Jyoshnavi, C., Swathi, D., Arun Kumar, T., Dharnasi, P., & Saravanan, M. (2026). A technology driven – solution for food and hunger management. International Journal of Computer Technology and Electronics Communication (IJCTEC), 9(2), 440–448.

31. Archana, R., & Anand, L. (2025). Residual u-net with Self-Attention based deep convolutional adaptive capsule network for liver cancer segmentation and classification. Biomedical Signal Processing and Control, 105, 107665.

32. Saravanan, M., Kumar, A. S., Devasaran, R., Seshadri, G., & Sivaganesan, S. (2019). Performance analysis of very sparse matrix converter using indirect space vector modulation. Intern. Jou. of Inn. Techn. and Expl. Eng, 9(1), 4756-4762.

33. Saravanan, M., & Sivakumaran, T. S. (2016). Three phase dual input direct matrix converter for integration of two AC sources from wind turbines. Circuits Syst., 7, 3807-3817.

34. Rakesh, V., Vinay Kumar, M., Bharath Patel, P., Varun Raj, B., Saravanan, M., & Dharnasi, P. (2026). IoT-based gas leakage detector with SMS alert. International Journal of Computer Technology and Electronics Communication (IJCTEC), 9(2), 449–456.