Glaucoma Detection Using Deep Learning and Prompt-Based Explainable Report Generation
Bibliographic record
Abstract
ABSTRACT Glaucoma is a leading cause of irreversible blindness and requires early detection to prevent vision loss. This study proposes a novel framework for automated glaucoma detection using fundus images, integrating deep learning and explainable artificial intelligence (XAI). By unifying five public datasets (RIM-ONE, ACRIMA, DRISHTI-GS, REFUGE, and EyePACS), we have created a diverse dataset to enhance model generalizability. An ensemble of five deep learning models, three convolutional neural networks (ResNet50, EfficientNet-B0, DenseNet121) and two transformer-based models (Vision Transformer, Swin Transformer) are trained for robust classification. Grad-CAM and attention rollout visualizations provided insight into model decision making, highlighting critical regions such as the optic disc and cup. These visualizations, combined with ensemble predictions, were processed by Google Gemini 1.5 Flash to generate clinician-style diagnostic reports. The ensemble model has achieved a test accuracy of 95.38% and an AUC of 0.99, outperforming individual models. This framework improves diagnostic accuracy and interpretability, bridging the gap between AI predictions and clinical utility, with potential for future integration into real-world ophthalmic workflows.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".