Journal of ISMAC
IRO Journals
Volume 7 — Issue 2 — June 2025

A Systematic Review on Diabetic Retinopathy Classification Using AI Techniques and IoMT

https://doi.org/10.36548/jismac.2025.2.005
Pages: 170-183
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Nithiya Priya D.
Ph.D Scholar, Interdisciplinary, SBV University
Mohamed Hanifah
Department of General Medicine, 4Department of Ophthalmology, Mahatma Gandhi Medical College and Research Institute
Ayyappan K.
Department of Electronics and Communication Engineering, Rajiv Gandhi College of Engineering and Technology,
Rajalakshmi A.R.
Department of Ophthalmology,
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How to Cite

D., Nithiya Priya, Mohamed Hanifah, Ayyappan K., and Rajalakshmi A.R. 2025. “A Systematic Review on Diabetic Retinopathy Classification Using AI Techniques and IoMT”. Journal of ISMAC 7 (2): 170-83. https://doi.org/10.36548/jismac.2025.2.005.
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Keywords

— Diabetic Retinopathy
— Artificial Intelligence
— Machine Learning
— Deep Learning
— IOMT
— Smartphone
Published: 08-07-2025

Abstract

Diabetic retinopathy is a complication in the retina of the eye due to the accumulation of high glucose levels among the Type 1 and Type 2 diabetes patients. It has also been noted that early diagnosis of the diabetic retinopathy helps patients overcome vision loss. The Internet of Medical Things (IoMT) is an emerging technology in the medical field that allows patients to send and receive medical data to hospitals and consult with doctors as needed. IoMT applications in diabetic retinopathy assist not only in early detection but also in diagnosis, enabling patients in the early stages to address vision problems. Therefore, this paper presents a survey of various machine learning, deep learning, combined AI techniques, the role of smartphones and handheld devices, and the application of IoMT in the early detection and diagnosis of diabetic retinopathy to mitigate vision loss in patients.

References

  1. M. Nahiduzzaman, M. R. Islam, S. M. R. Islam, M. O. F. Goni, M. S. Anower and K. -S. Kwak, "Hybrid CNN-SVD Based Prominent Feature Extraction and Selection for Grading Diabetic Retinopathy Using Extreme Learning Machine Algorithm," in IEEE Access, vol. 9, 2021, 152261-152274.doi: 10.1109/ACCESS.2021.3125791.
  2. M. A. Urina-Triana et al., "Machine Learning and AI Approaches for Analyzing Diabetic and Hypertensive Retinopathy in Ocular Images: A Literature Review," in IEEE Access, vol. 12, 2024, 54590-54607, doi: 10.1109/ACCESS.2024.3378277.
  3. Chaudhary, Sonali, and H. R. Ramya. "Detection of diabetic retinopathy using machine learning algorithm." In 2020 IEEE international conference for innovation in technology (INOCON), IEEE, 2020, 1-5.
  4. Soni, Akanksha, and Avinash Rai. "A novel approach for the early recognition of diabetic retinopathy using machine learning." In 2021 international conference on computer communication and informatics (ICCCI), IEEE, 2021, 1-5.
  5. Li, Wanyue, Yanan Song, Kang Chen, Jun Ying, Zhong Zheng, Shen Qiao, Ming Yang, Maonian Zhang, and Ying Zhang. "Predictive model and risk analysis for diabetic retinopathy using machine learning: a retrospective cohort study in China." Bmj Open 11, no. 11 (2021): e050989.
  6. Alabdulwahhab, K. M., W. Sami, T. Mehmood, S. A. Meo, T. A. Alasbali, and F. A. Alwadani. "Automated detection of diabetic retinopathy using machine learning classifiers." European Review for Medical & Pharmacological Sciences 25, no. 2 (2021).
  7. Mujeeb Rahman, K. K., Mohamed Nasor, and Ahmed Imran. "Automatic screening of diabetic retinopathy using fundus images and machine learning algorithms." Diagnostics 12, no. 9 (2022): 2262.
  8. Math, Laxmi, and Ruksar Fatima. "Adaptive machine learning classification for diabetic retinopathy." Multimedia Tools and Applications 80, no. 4 (2021): 5173-5186.
  9. Parthasharathi, G. U., R. Premnivas, and K. Jasmine. "Diabetic retinopathy detection using machine learning." Journal of Innovative Image Processing 4, no. 1 (2022): 26-33.
  10. Sumathy, B., Arindam Chakrabarty, Sandeep Gupta, Sanil S. Hishan, Bhavana Raj, Kamal Gulati, and Gaurav Dhiman. "Prediction of diabetic retinopathy using health records with machine learning classifiers and data science." International Journal of Reliable and Quality E-Healthcare (IJRQEH) 11, no. 2 (2022): 1-16.
  11. Mahmoud, Mohamed H., Salman Alamery, Hassan Fouad, Amir Altinawi, and Ahmed E. Youssef. "An automatic detection system of diabetic retinopathy using a hybrid inductive machine learning algorithm." Personal and Ubiquitous Computing (2023): 1-15.
  12. Reddy, G. Thippa, Sweta Bhattacharya, S. Siva Ramakrishnan, Chiranji Lal Chowdhary, Saqib Hakak, Rajesh Kaluri, and M. Praveen Kumar Reddy. "An ensemble based machine learning model for diabetic retinopathy classification." In 2020 international conference on emerging trends in information technology and engineering (ic-ETITE), IEEE, 2020, 1-6.
  13. Nawaz, Fouzia, Muhammad Ramzan, Khalid Mehmood, Hikmat Ullah Khan, Saleem Hayat Khan, and Muhammad Raheel Bhutta. "Early Detection of Diabetic Retinopathy Using Machine Intelligence through Deep Transfer and Representational Learning." Computers, Materials & Continua 66, no. 3 (2021).
  14. Sharma, Ayushi, Swapnil Shinde, Imran Ismail Shaikh, Madhav Vyas, and Soumya Rani. "Machine learning approach for detection of diabetic retinopathy with improved pre-processing." In 2021 International conference on computing, communication, and intelligent systems (ICCCIS), IEEE, 2021, 517-522.
  15. Gonçalves, Mariana Batista, Luis Filipe Nakayama, Daniel Ferraz, Hanna Faber, Edward Korot, Fernando Korn Malerbi, Caio Vinicius Regatieri et al. "Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: A review." Eye 38, no. 3 (2024): 426-433.
  16. Bora, Ashish, Siva Balasubramanian, Boris Babenko, Sunny Virmani, Subhashini Venugopalan, Akinori Mitani, Guilherme de Oliveira Marinho et al. "Predicting the risk of developing diabetic retinopathy using deep learning." The Lancet Digital Health 3, no. 1 (2021): e10-e19.
  17. Mushtaq, Gazala, and Farheen Siddiqui. "Detection of diabetic retinopathy using deep learning methodology." In IOP conference series: materials science and engineering, vol. 1070, no. 1, IOP Publishing, 2021, 012049.
  18. Gundluru, Nagaraja, Dharmendra Singh Rajput, Kuruva Lakshmanna, Rajesh Kaluri, Mohammad Shorfuzzaman, Mueen Uddin, and Mohammad Arifin Rahman Khan. "Enhancement of detection of diabetic retinopathy using Harris hawks optimization with deep learning model." Computational Intelligence and Neuroscience 2022, no. 1 (2022): 8512469.
  19. Harshitha, Chava, Alla Asha, Jangala Lakshmi Sai Pushkala, Rayapudi Naga Swetha Anogini, and C. Karthikeyan. "Predicting the stages of diabetic retinopathy using deep learning." In 2021 6th international conference on inventive computation technologies (ICICT), IEEE, 2021, 1-6.
  20. Zhang, Xiao, Fan Li, Donghong Li, Qijie Wei, Xiaoxu Han, Bilei Zhang, Huan Chen et al. "Automated detection of severe diabetic retinopathy using deep learning method." Graefe's Archive for Clinical and Experimental Ophthalmology (2022): 1-8.
  21. Joshi, Shivani, Rajiv Kumar, Praveen Kumar Rai, and Sanskar Garg. "Diabetic retinopathy using deep learning." In 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), IEEE, 2023, 145-149.
  22. Gunasekeran, Dinesh V., Daniel SW Ting, Gavin SW Tan, and Tien Y. Wong. "Artificial intelligence for diabetic retinopathy screening, prediction and management." Current opinion in ophthalmology 31, no. 5 (2020): 357-365.
  23. Grauslund, Jakob. "Diabetic retinopathy screening in the emerging era of artificial intelligence." Diabetologia 65, no. 9 (2022): 1415-1423.
  24. Vujosevic, Stela, Stephen J. Aldington, Paolo Silva, Cristina Hernández, Peter Scanlon, Tunde Peto, and Rafael Simó. "Screening for diabetic retinopathy: new perspectives and challenges." The Lancet Diabetes & Endocrinology 8, no. 4 (2020): 337-347.
  25. Ishtiaq, Uzair, Sameem Abdul Kareem, Erma Rahayu Mohd Faizal Abdullah, Ghulam Mujtaba, Rashid Jahangir, and Hafiz Yasir Ghafoor. "Diabetic retinopathy detection through artificial intelligent techniques: a review and open issues." Multimedia Tools and Applications 79 (2020): 15209-15252.
  26. Rajesh, Anand E., Oliver Q. Davidson, Cecilia S. Lee, and Aaron Y. Lee. "Artificial Intelligence and Diabetic Retinopathy: AI Framework, prospective studies, head-to-head validation, and cost-effectiveness." Diabetes care 46, no. 10 (2023): 1728-1739.
  27. Poly, Tahmina Nasrin, Md Mohaimenul Islam, Bruno Andreas Walther, Ming Chin Lin, and Yu-Chuan Jack Li. "Artificial intelligence in diabetic retinopathy: Bibliometric analysis." Computer Methods and Programs in Biomedicine 231 (2023): 107358.
  28. Selvachandran, Ganeshsree, Shio Gai Quek, Raveendran Paramesran, Weiping Ding, and Le Hoang Son. "Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods." Artificial intelligence review 56, no. 2 (2023): 915-964.
  29. Grzybowski, Andrzej, Panisa Singhanetr, Onnisa Nanegrungsunk, and Paisan Ruamviboonsuk. "Artificial intelligence for diabetic retinopathy screening using color retinal photographs: from development to deployment." Ophthalmology and Therapy 12, no. 3 (2023): 1419-1437.
  30. Shahzad, Tariq, Muhammad Saleem, Muhammad Sajid Farooq, Sagheer Abbas, Muhammad Adnan Khan, and Khmaies Ouahada. "Developing a transparent diagnosis model for diabetic retinopathy using explainable AI." IEEE Access (2024).
  31. Rajalakshmi, Ramachandran, Thyparambil Aravindakshan PramodKumar, Abdul Subhan Naziyagulnaaz, Ranjit Mohan Anjana, Rajiv Raman, Suchetha Manikandan, and Viswanathan Mohan. "Leveraging artificial intelligence for diabetic retinopathy screening and management: history and current advances." In Seminars in Ophthalmology, Taylor & Francis, 2024, 1-8.
  32. Mohammadi, S. Saeed, and Quan Dong Nguyen. "A user-friendly approach for the diagnosis of diabetic retinopathy using ChatGPT and automated machine learning." Ophthalmology Science 4, no. 4 (2024): 100495.
  33. Bidwai, Pooja, Shilpa Gite, Kishore Pahuja, and Ketan Kotecha. "A systematic literature review on diabetic retinopathy using an artificial intelligence approach." Big Data and Cognitive Computing 6, no. 4 (2022): 152.
  34. Sebastian, Anila, Omar Elharrouss, Somaya Al-Maadeed, and Noor Almaadeed. "A survey on deep-learning-based diabetic retinopathy classification." Diagnostics 13, no. 3 (2023): 345.
  35. Kukkar, Ashima, Dinesh Gupta, Shehab Mohamed Beram, Mukesh Soni, Nikhil Kumar Singh, Ashutosh Sharma, Rahul Neware, Mohammad Shabaz, and Ali Rizwan. "Optimizing deep learning model parameters using socially implemented IoMT systems for diabetic retinopathy classification problem." IEEE Transactions on Computational Social Systems 10, no. 4 (2022): 1654-1665.