International Journal of Computer Techniques Volume 12 Issue 3 | “ResNet-Powered Retinal Image Analysis”
ResNet-Powered Retinal Image Analysis
International Journal of Computer Techniques – Volume 12 Issue 3, May – June 2025
Abstract
Diabetic Retinopathy (DR) is a severe eye condition that requires **early detection to prevent vision loss**. This study presents **RetinoNet**, an AI-powered system utilizing **ResNet architecture** to analyze **retinal images** efficiently. By leveraging deep learning techniques, the model provides **accurate, accessible, and rapid DR screening**, reducing the dependence on specialized eye professionals in resource-limited areas.
Keywords
Diabetic Retinopathy, Deep Learning, Retinal Image Analysis, Automated Diagnosis, Medical Imaging, AI in Healthcare, Ophthalmology.
Conclusion
The **ResNet-based RetinoNet model** demonstrates **exceptional accuracy**, reaching **99.5% training accuracy and 99.1% validation accuracy** on the **APTOS 2019 Blindness Detection dataset**. Future improvements will focus on **real-time deployment, mobile applications, and broader dataset integration** for enhanced robustness.
References
- Phan, U. et al. (2025). “Progressive Transfer Learning for Multi-Pass Fundus Image Restoration.” arXiv.
- Raj, G. M. et al. (2024). “Federated Learning for Diabetic Retinopathy Diagnosis.” arXiv.
- Dai, L. et al. (2021). “A Deep Learning System for Detecting Diabetic Retinopathy Across the Disease Spectrum.” Nature Communications.
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