Statistical features selection from intrinsic mode functions for pathologies detection in retina digital images
Bibliographic record
Abstract
The empirical mode decomposition (EMD) is employed to analyze retina digital images in the frequency domain and statistical features are extracted from high frequency components of the analyzed images. The purpose is to classify normal versus abnormal images. Three different pathologies are considered including, circinates, drusens, and microaneurysms (MA). The most informative and non redundant features are ranked and selected by use of statistical features selection techniques; namely t-statistic, entropy, Battacharrayia statistic, the area between the receiver operating characteristic (ROC) and principal component analysis (PCA). Finally, support vector machines (SVM) with polynomial and radial basis function (RBF) kernels are used to classify retina digital images based on the selected features. The simulation results from leave-one-out method (LOOM) show the effectiveness of the EMD-Battacharrayia-SVM achieves 96.54%±0.0293 correct classification rate. Thus, features selection helps improving the accuracy of our system designed for pathologies detection in retina.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".