Computer Aided Diagnostic Techniques in Automated Detection of Eye Related Diseases - A Comparative Study
Bibliographic record
Abstract
World Health Organization (WHO) in a new study has recognized eye related defects to be one of the primary health challenges faced by the existing society. Common retinal abnormalities include Glaucoma, Diabetic retinopathy and Macular degeneration. Retinal eye defects have significantly increased in the last decade across developing and developed countries. These defects if not diagnosed and treated at the appropriate time, would result in complete loss of vision. Diabetic retinopathy is predominantly common among diabetic patients. In Macular Degeneration, the central part of the retina is widely affected. In this case, the retinal cells deteriorate and images are not established correctly. The CAD system for eye diseases falls under the Supervised Learning techniques. This technique refers to methods that enable creation of a correlation with different features and labelled outcomes. Few of these include KNN, Naïve Bayes, Adaboost Classifiers, tree-based Classifier, ELM classifier, SVM and LIBSVM classifier etc. The main objective of this paper is to summarize the various CAD techniques adopted for early detection of eye diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".