Bibliographic record
Abstract
Glaucoma is one of the leading causes of irreversible blindness around the world. One of the significant indicators of Glaucoma is the enlargement of the Optic Cup (OC) to the Optic Disc (OD). In this project, we created a computer algorithm to identify the ratio between the sizes of OD and OC to further predict the likelihood of glaucoma. The algorithm consisted of a two-step approach. The first step of the approach involved the identification and cropping of the Optic Nerve Head area, the part that contains the OD and OC in the retina. This was done using thresholding based on the brightest spot in the image. The second step was to segment the OD and OC from the cropped image. We used an image augmentation library to transform the images in our dataset to add variability. We passed the transformed images into a deep learning algorithm known as U-Net to segment the OD and OC. Our analysis in training separate deep learning models for OD and OC yielded a higher accuracy as compared to training the models for OD and OC together. Our model has the potential of performing better with greater precision to segment images for diagnosing Glaucoma. The future work includes enhancing the model by making some additional changes to increase the model’s accuracy. Department: Computer Science Faculty Mentor: Dr. Dana Cobzas
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 | 0.000 | 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".