International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Artificial Intelligence in Ophthalmology: Early Screening of Diabetic Retinopathy from Retinal Scans |
|---|---|
| ABSTRACT | Diabetic Retinopathy is a vision-affecting condition that afflicts individuals with diabetes, often resulting from elevated blood sugar levels that harm the eye's blood vessels, potentially leading to blindness. The primary goal is to assess the severity of diabetic retinopathy, distinguishing between mild, moderate, and severe cases based on the presence of exudates and micro aneurysms in these images. To achieve this, the proposed method combines image processing, feature extraction, and machine learning models to accurately predict the presence of exudates and micro aneurysms, which are then used for grading. Exudate grading considers their proximity to the macula, while micro aneurysm grading is determined by their quantity. |
| AUTHOR | KOLLA YASWANTH, UDAY KIRAN KODATI, YADAVALLI VISHNU VARDHAN REDDY, DR.M.HEMALATHA UG Students, Dept. of CSE, R.M.D. Engineering College, Tiruvallur, Tamil Nadu, India Professor, Dept. of CSE., R.M.D. Engineering College, Tiruvallur, Tamil Nadu, India |
| VOLUME | 181 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1402006 |
| pdf/6_Artificial Intelligence in Ophthalmology Early Screening of Diabetic Retinopathy from Retinal Scans.pdf | |
| KEYWORDS | |
| References | 1. Akter, M., Uddin, M. S., Khan, M. H.: Morphology-based exudates detection from color fundus images in diabetic retinopathy. In: 2021 International Conference on Electrical Engineering and Information & Communication Technology, IEEE, pp. 1–4, (2024) 2. Aleem, S., Sheng, B., Li, P., Yang, P., Feng, D.D.: Fast and accurate retinal identifcation system: using retinal blood vasculature landmarks. IEEE Trans. Indus. Inform. 15(7), 4099–4110 (2023) 3. Bouveyron, C., Girard, S., Schmid, C.: Class-specifc subspace discriminant analysis for high-dimensional data. In: International Statistical and Optimization Perspectives Workshop Subspace, Latent Structure and Feature Selection, pp. 139–150. Springer, Amsterdam (2022) C. P. Wilkinson et al., ‘‘Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales,’’ Ophthalmology, vol. 110, no. 9, pp. 1677–1682, Sep. 2021. 4. Carrera, E. V., González, A., Carrera, R.: Automated detection of diabetic retinopathy using svm. In: 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), IEEE, pp. 1–4 (2020) 5. Chandran, A., Nisha, K., Vineetha, S.: Computer aided approach for proliferative diabetic retinopathy detection in color retinal images. In: 2016 International Conference on Next Generation Intelligent Systems (ICNGIS), IEEE, pp. 1–6 (2019) 6. Choudhury, S., Bandyopadhyay, S., Latib, S., Kole, D., Giri, C.: Fuzzy c means based feature extraction and classifcation of diabetic retinopathy using support vector machines. In: 2016 International Conference on Communication and Signal Processing (ICCSP), IEEE, pp. 1520–1525 (2016) 7. V. Deepika, R. Dorairaj, K. Namuduri, and H. Thompson, ‘‘Automated detection and classification of vascular abnormalities in diabetic retinopathy,’’ in Proc. 38th Asilomar Conf. Signals, Syst. Comput., vol. 2, 2004, pp. 1625–1629. |
