A Novel Approach for Glaucoma Classification by Wavelet Neural Networks Using Statistical and Graph-Based Features of Qualitatively Improved Images
Bibliographic record
Abstract
In this paper, we have proposed a new glaucoma classification approach that employs a wavelet neural network (WNN) on optimally enhanced retinal images' features.To avoid tedious and error-prone manual analysis of retinal images, computer-aided diagnosis (CAD) substantially aids in robust diagnosis.Our proposal has a significant contribution to make in all major CAD phases.Retinal image quality improvement is attempted in two phases.The retinal image preprocessing phase improves the brightness and contrast of the image through quantile-based histogram modification.It is followed by the image structural enhancement phase, which involves multi-scale morphological operations using image-specific dynamic structuring elements (SE).Graph-based retinal image features in terms of Local Graph Structures (LGS) and Graph Shortest Path (GSP) statistics are extracted from various directions along with the statistical features from the enhanced images.WNN is employed to classify glaucoma retinal images with a suitable wavelet activation function.The performance of the WNN classifier is compared with that of multilayer perceptron neural networks (MLP) and the state-of-the art approaches to prove its efficacy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".