Preliminary Diagnostic System for the Classification of Senile Cataract Using Convolutional Neural Networks
Bibliographic record
Abstract
This study proposed an AI system to diagnose cataracts using Neural Networks.The development of the system was carried out following the agile Scrum framework, including the development of artefacts defined by the Rational Unified Process software development process.10 sprints were defined to complete the software development with the defined artefacts.Two methods were described to validate the model and the system in general.The precision, recall and F1-Score metrics were also determined to evaluate the performance and effectiveness of the model in diagnosing cataracts.Cataract classification yields 80% of positive cases found and 20% of positive cases not found.The proposed system uses fundus images to diagnose cataracts through the smartphone camera, obtain an automatic diagnosis and report, and assign an ophthalmologist to give the verdict.For the "Normal" classification, 93% of positive cases were found, while 7% were not.The average number of positive cases found is 86%, with 14% of positive cases not found.In all cases, we have a percentage of more than 80%.After obtaining the results using the established indicators, it is deduced that the preliminary diagnosis system can be considered support so that the doctor's activity is more optimal.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".