A Survey on Deep-Learning-Based Diabetic Retinopathy Classification
importance 7/10
ai assisted
Retinal Imaging and Analysis ophthalmology / diabetes care stage: research
Problem
Diabetic retinopathy damages the eyes of long-term diabetes patients, and early automatic detection is needed to avoid vision loss.
Why it matters
Diabetic retinopathy can lead to blindness; early detection helps avoid complications.
What the AI did
The paper surveys deep-learning methods for DR detection from fundus images and compares their performance on common computer-vision metrics and datasets.
Results
not stated in source
Impact
Synthesizes and compares existing DL DR-detection methods; no new clinical result claimed.
Record
| Venue | Diagnostics |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Anila Sebastian, Omar Elharrouss, Somaya Ali Al-Maadeed, Noor Almaadeed |
| Citations | 110 (OpenAlex, at import time) |
| References | 121 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/diagnostics13030345 |
| Open access | gold |
| Keywords | diabetic retinopathy, deep learning, fundus images, survey, classification |
Importance rating
7
significant — thousands to millions affected
Abstract states diabetes is considerably increasing worldwide and diabetic retinopathy damages eyes and can cause loss of vision; early detection helps avoid complications. Major health impact, though this paper is a survey.