Sharper Detection: Enhanced Techniques for Diabetic Retinopathy Grading
Bibliographic record
Abstract
Diabetic retinopathy (DR) is a leading cause of blindness, and deep learning methods are extensively used for automated DR grading. This paper demonstrates how applying techniques such as oversampling, data augmentation, K-fold cross-validation, learning rate scheduling, and early stopping can significantly improve performance for models such as ResNet18 and ResNet50. Using the APTOS and EyePACS datasets, our results reveal a substantial accuracy improvement compared to prior methods, which rely on oversampling and data augmentation as baseline techniques. We obtained an accuracy of 97.78% on APTOS with ResNet18 and 93.80% on EyePACS with ResNet50, outperforming the baseline. The techniques presented offer a valuable framework that can complement and enhance the state-of-the-art methods for DR grading and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".