Exudates Detection Based on SSD MobileNet for Referable Diabetic Retinopathy
Bibliographic record
Abstract
Automatic detection of the referable Diabetic Retinopathy (RDR) has become essential in diabetic patients, especially who live in the remote regions, to avoid a serious visual impairment. For this reason, different approaches have been developed with the aim of detect and segment the principal DR lesions for automatic diagnosis of the RDR. Exudate is one of the DR lesions and if these lesions appear in the macular region, a diabetic macular edema (DME) can be suspected and a detailed analysis by ophthalmologist is required. Then it is important to detect these lesions with their position related to the macular region to determine the danger level. This paper presents an automatic method to localize the exudates and optic disc (OD) using Single Shot Detector (SSD) scheme based on MobileNet-V1 as base network to determine if the risk of DME exits to indicate patients the necessity of consultation by ophthalmologist. The proposed system is evaluated using MESSIDOR Database, providing 89.15% accuracy, 88.17% sensitivity and 91.67% specificity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".