Classical Machine Learning Based Diabetic Retinopathy Detection Using Handcrafted Features
Bibliographic record
Abstract
Diabetic retinopathy (DR) is a progressive complication of diabetes and a leading cause of vision impairment worldwide, highlighting the need for reliable and explainable automated screening systems.This article investigates a classical feature-based machine learning (ML) framework for DR detection using retinal fundus images.Specifically, two complementary handcrafted feature descriptors are used: Binary Patterns Pyramid (BPP) for texture representation and Pyramid Histogram of Oriented Gradients (PHOG) for structural and edge information.These features are extracted to model the retinal class distributions.The extracted features are evaluated using multiple supervised ML classifiers, including Bayesian Network (BN), Naï ve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), AdaBoost J48 (AJJ48), J48 Decision Tree (J48), and Random Forest (RF).Experimental evaluation is conducted on the Kaggle DR detection dataset using 10-fold cross-validation.The results demonstrate that classical features combined with appropriate classifiers can achieve competitive performance, with SVM, RF, and BN yielding the highest accuracies.The evaluation in this article highlights the importance of the classical yet simple features for DR detection, particularly in scenarios with limited data availability and limited computing resources.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".