Sustainable deep learning approach using hybrid xception VGG19 for renal disease detection

Abstract— This study introduces a hybrid deep learning
system that incorporates the Xception and VGG19 architectures
to improve retinal images and help diagnose hypertensive
retinopathy early, in line with the UN's Sustainable
Development Goals. The suggested solution fixes medical
imaging issues caused by poor lighting and noise. Vasculature,
contrast, and texture are improved, making fundus images
easier to see. The quantitative analysis demonstrates that
VGG19 performs well with a PSNR of 49.45 dB, SSIM of 0.993,
and MSE of 0.71. This ensures diagnosis structure and
accuracy. Since ophthalmologists can see vascular issues
better, they can detect problems earlier. This research
improves SDG 3 (Good Health and Well-Being) by developing
cost-effective, AI-driven eye screening protocols to prevent
hypertension-related vision loss. The energy-efficient and
scalable technique aids SDG 10 (Reduced Inequalities) by
making diagnostic testing easier for poor people and SDG 9
(Industry, Innovation, and Infrastructure) by making AI
technologies in healthcare systems easier to utilize. The
proposed Xception-VGG19 fusion technique highlights how AI
may improve retinal images and forecast diseases in medical
imaging. This encourages equitable and smart healthcare.