Optoelectronic Retinal Images for the Prediction of Diabetic Macular Edema Based on a Hybrid Deep Transfer Learning Technique
Bibliographic record
Abstract
Diabetes is characterized by elevated levels of glucose in the blood, which can lead to complications like Diabetic Macular Edema (DME), causing permanent vision loss.A novel HET-EYE-NETS which is built on the ensemble transfer learning networks with extreme feedforward model for the prediction of DME is proposed.The proposed algorithm preprocesses the color optoelectronic retinal images and classifies the severity of DME by the three-stage pipeline model.In the first stage, DME is segmented by the U-Nets, features of segmented DME are extracted by AlexNet layers and finally severity is predicted by the extreme learning feedforward layers.The extensive experimentation is carried out using IDRiD and MESSIDOR database images.During this process, performance measures like precision, recall, F1-score, specificity, and accuracy are computed and analyzed.In addition, data augmentation is employed to address the issue of data imbalance problem in IDRiD and MESSIDOR database images.The proposed HET-EYE-NETS model achieved an average accuracy of 99.1%, precision of 99.2%, recall of 99% and F1-score of 0.9920.Results proved that proposed HET-EYE-NETS model outperforms existing learning models, demonstrating its potential for early diagnosis of DME.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".