TY  - EJOU
AU  - Palaniappan, Damodharan 
AU  - Tak, Tan Kuan 
AU  - Vijayan, K. 
AU  - Maram, Balajee 
AU  - Kshirsagar, Pravin R 
AU  - Ahmad, Naim 

TI  - Enhancement of Medical Imaging Technique for Diabetic Retinopathy: Realistic Synthetic Image Generation Using GenAI
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 145
IS  - 3
SN  - 1526-1506

AB  - 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 <mml:math id="mml-ieqn-1"><mml:mo>±</mml:mo></mml:math> 42.67), while SSIM remains high across phases. Expert review shows substantial agreement (Cohen’s <mml:math id="mml-ieqn-2"><mml:mi>κ</mml:mi></mml:math> = 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.
KW  - Diabetic retinopathy; synthetic image generation; GenAI; medical imaging; ultra-widefield retinal imaging; enhanced medical imaging datasets; multi-scale structural similarity

DO  - 10.32604/cmes.2025.073387