Enhanced Cardiovascular Disease Risk Prediction from Retinal Vasculature using DenseNet121 in DR and HR Patients
DOI:
https://doi.org/10.15662/IJEETR.2026.0802030Keywords:
Cardiovascular Disease (CVD), DenseNet121, Deep Learning, ResNet-50, Diabetic Retinopathy (DR), Hypertensive Retinopathy (HR)Abstract
Cardiovascular disease (CVD) is one of the leading causes of mortality worldwide, and early detection is essential for effective treatment and prevention. Retinal imaging has emerged as a noninvasive method for identifying vascular changes associated with cardiovascular risk. The existing system uses the CVDNet framework based on the ResNet50 architecture to analyze retinal fundus images and predict cardiovascular risk in patients with diabetic retinopathy (DR) and hypertensive retinopathy (HR). Although this approach provides promising results, it may suffer from limited feature reuse and higher parameter redundancy. To address these limitations, the proposed system introduces a DenseNet121–based architecture for improved feature extraction and classification. DenseNet121 enables dense connections between layers, allowing better gradient flow and feature reuse. This architecture reduces the number of parameters while improving learning efficiency. The model analyzes retinal vascular structures and automatically learns discriminative patterns associated with cardiovascular risk. It enhances prediction accuracy and reduces over fitting, especially in medical datasets. The proposed system aims to provide a more reliable, noninvasive, and efficient diagnostic tool for early CVD risk detection. This approach can support clinicians in decision making and preventive care. Overall, the DenseNet121–based system offers improved performance compared to traditional deep learning models
References
1. M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, "Toward 6G networks: Use cases and technologies," *IEEE Communications Magazine*, vol. 58, no. 3, pp. 55–61, Mar. 2020.
2. A. S. Valdez-Guerrero, J. C. Quintana-Pérez, M. G. Arellano-Mendoza, F. J. Castañeda-Ibarra, F. Tamay-Cach, and D. Alemán-González-Duhart, "Diabetic retinopathy: Important biochemical alterations and the main treatment strategies," *Canadian Journal of Diabetes*, vol. 45, no. 6, pp. 504–511, Aug. 2021.
3. T. R. Gadekallu, N. Khare, S. Bhattacharya, S. Singh, P. K. R. Maddikunta, and G. Srivastava, "Deep neural networks to predict diabetic retinopathy," *Journal of Ambient Intelligence and Humanized Computing*, vol. 14, no. 5, pp. 5407–5420, May 2023.
4. X.-H. Xu, B. Sun, S. Zhong, D.-D. Wei, Z. Hong, and A.-Q. Dong, "Diabetic retinopathy predicts cardiovascular mortality in diabetes: A meta-analysis," *BMC Cardiovascular Disorders*, vol. 20, no. 1, pp. 1–8, Dec. 2020.
5. D. Y. L. Wong, M. C. Lam, A. Ran, and C. Y. Cheung, "Artificial intelligence in retinal imaging for cardiovascular disease prediction: Current trends and future directions," *Current Opinion in Ophthalmology*, vol. 33, no. 5, pp. 440–446, 2022.
6. W. Hu, F. S. L. Yii, R. Chen, X. Zhang, X. Shang, K. Kiburg, E. Woods, A. Vingrys, L. Zhang, Z. Zhu, and M. He, "A systematic review and meta-analysis of applying deep learning in the prediction of the risk of cardiovascular diseases from retinal images," *Translational Vision Science & Technology*, vol. 12, no. 7, p. 14, Jul. 2023.
7. R. Poplin, A. V. Varadarajan, K. Blumer, Y. Liu, M. V. McConnell, G. S. Corrado, L. Peng, and D. R. Webster, "Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning," *Nature Biomedical Engineering*, vol. 2, no. 3, pp. 158–164, Feb. 2018.
8. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
9. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
10. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
11. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
12. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
13. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
14. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
15. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
16. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
17. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
18. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
19. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
20. C. Y. Cheung et al., "A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre," *Nature Biomedical Engineering*, vol. 5, no. 6, pp. 498–508, Oct. 2020.
21. C. French, R. P. Cubbidge, and R. Heitmar, "The application of arteriovenous ratio (AVR) cut-off values in clinic to stratify cardiovascular risk in patients," *Ophthalmic and Physiological Optics*, vol. 42, no. 4, pp. 666–674, Jul. 2022, doi: 10.1111/opo.12967.
22. S. P. Rajan, "Recognition of cardiovascular diseases through retinal images using optic cup to optic disc ratio," *Pattern Recognition and Image Analysis*, vol. 30, no. 2, pp. 256–263, Apr. 2020
