Detection and diagnosis of diabetic retinopathy in retinal fundus images using agentic AI approaches
R Sathya, A Valaramathi · 2025
Read Detection and diagnosis of diabetic retinopathy in retinal fundus images using agentic AI approaches by R Sathya, A Valaramathi (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12852743.txt.
Description
Sathya R and A Valaramathi build an agent-based system to spot diabetic retinopathy in eye scans. The disease is a leading cause of vision loss, and early catches save sight. Standard learning systems classify well but explain little and adapt poorly across patients. Their framework, AADR-AI, gives the analysis to a team of cooperating agents that blend convolutional and transformer networks under a decision fusion layer.
The agents act with autonomy, reacting to each image and adjusting their feature learning to the patient's own pattern. In tests on standard datasets, the system reached accuracy up to 96.7 percent with lower computing cost than rivals. The authors credit the agent design for real-time handling of varied image quality and disease stages. They present it as a step toward decision support that doctors can question and trust.
Catalog record
| CREATOR | R Sathya, A Valaramathi |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 63 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |