Automatic Digital Analysis System to Grade Diabetic Retinopathy by Integrated Stacking Model Concept
Bibliographic record
Abstract
Ophthalmic diagnosis is based primarily on visual information (photographs of the retina), and the recent availability of digital fundus images allows such quantization of parameters or classification to be derived from computerized image processing.Thus, an Automated Digital Analysis System (ADAS) is designed for grading Diabetic Retinopathy (DR) in this work.The Integrated Stacking Model (ISM) concept is employed to design a single large multi-headed deep learning model.Each sub-model has three layers with a predefined number of 33 convolution filters (128, 256 and 512) and a pooling layer to abstract deep features.The outputs of sub-models are fed to the meta-learner for grading the DR.The performance of the ADAS for grading DR is evaluated using MESSIDOR-1 and Kaggle datasets.For the images in MESSIDOR-1, the proposed system is considered a four-class problem, and it is a five-class problem for Kaggle dataset images.Results show that the proposed ISM-DR classification system provides promising results with an average classification accuracy of 99.2% (four-class) and 99.1% (five-class) using MESSIDOR-1 and Kaggle dataset images respectively when stacking five sub-models.A clear trend can be seen from the experimental results towards better performance when more sub-models are stacked.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".