@article{Palaniappan2025, 
author = {Damodharan Palaniappan and Tan Kuan Tak and K. Vijayan and Balajee Maram and Pravin R Kshirsagar and Naim Ahmad},
title = {Enhancement of Medical Imaging Technique for Diabetic Retinopathy: Realistic Synthetic Image Generation Using GenAI},
year = {2025},
journal = {Computer Modeling in Engineering & Sciences},
volume = {145},
number = {3},
pages = {4107-4127},
keywords = {Diabetic retinopathy, synthetic image generation, GenAI, medical imaging, ultra-widefield retinal imaging, enhanced medical imaging datasets, multi-scale structural similarity},
url = {https://www.sciopen.com/article/10.32604/cmes.2025.073387},
doi = {10.32604/cmes.2025.073387},
abstract = {A phase-aware cross-modal framework is presented that synthesizes UWF_FA from non-invasive UWF_RI for diabetic retinopathy (DR) stratification. A curated cohort of 1198 patients (2915 UWF_RI and 17,854 UWF_FA images) with strict registration quality supports training across three angiographic phases (initial, mid, final). The generator is based on a modified pix2pixHD with an added Gradient Variance Loss to better preserve microvasculature, and is evaluated using MAE, PSNR, SSIM, and MS-SSIM on held-out pairs. Quantitatively, the mid phase achieves the lowest MAE (98.76  ± 42.67), while SSIM remains high across phases. Expert review shows substantial agreement (Cohen’s  κ = 0.78–0.82) and Turing-style misclassification of 50%–70% of synthetic images as real, indicating strong perceptual realism. For downstream DR stratification, fusing multi-phase synthetic UWF_FA with UWF_RI in a Swin Transformer classifier yields significant gains over a UWF_RI-only baseline, with the full-phase setting (Set D) reaching AUC = 0.910 and accuracy = 0.829. These results support synthetic UWF_FA as a scalable, non-invasive complement to dye-based angiography that enhances screening accuracy while avoiding injection-related risks.}
}