Generative Adversarial Network for Retinal Fundus Image Synthesis in Diabetic Retinopathy Detection
Diabetic Retinopathy, GAN, Synthetic Retinal Image Generation, Data Augmentation, Deep Learning, Multiclass Classification, Medical Imaging.
Diabetic Retinopathy (DR) is one of the principal causes of sight loss in diabetic patients and its early diagnosis is the key to the prevention of irreversible blindness. Nevertheless, medical imaging data have the disadvantage of low data volumes and extreme class imbalance, which has an adverse impact on the performance of deep learning models. To overcome this problem, this study will come up with a Generative Adversarial Network (GAN)-based solution to synthetic retinal fundus image generation to improve multiclass diabetic retinopathy classification.
The GAN model is trained to build the distribution of real retinal images and produce synthetic fundus images of high-quality to match various levels of DR severity, namely, Normal, Mild, Moderate, Severe, and Proliferative DR. The synthetic images generated are added to the original dataset to enhance the equal representation of classes and diversity of data. An augmented dataset is then trained in a classification model and assessed using performance metrics that include accuracy, precision, recall, F1-score and ROC-AUC.Experimental findings indicate that augmented dataset achieved a better classification robustness and minimized overfitting when compared to traditional data augmentation methods. The recommended strategy emphasizes the use of adversarial learning as an effective method to improve the quality of the data set and to facilitate an efficient automated system of severity grading of DR in clinical practice.
Registration ID: IJVRA_702510 Published ID: IJVRA2603877
"Generative Adversarial Network for Retinal Fundus Image Synthesis in Diabetic Retinopathy Detection", IJVRA - International Journal of Versatile Research and Analysis (www.IJVRA.org), ISSN:2984-8903, Vol.4, Issue 3, page no.748-755, March-2026, Available :https://ijpub.org/IJVRA/papers/IJVRA2603877.pdf
Paper Reg. ID: IJVRA_702510
Published Paper Id: IJVRA2603877
Research Area: Other area not in list
Country: Mumbai, Maharashtra, India
ISSN: 2984-8903 | IMPACT FACTOR: 9.12 Calculated By Google Scholar | ESTD YEAR: 2023
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Publisher: IJVRA (IJ Publication) Janvi Wave
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