Automatic Detection of Exudates in Retinal Image Using Statistical Techniques
Bibliographic record
Abstract
Diabetic retinopathy is an eye disease seen widely in diabetes patient.It is one of the leading causes of vision loss or blindness characterised by exudates as one of its symptoms.In this paper an objective is to develop algorithm for exudates detection in poorly contrasted or low-quality images.To identify the exudates, initially pre-processing operation is performed on retinal images to retrieve the colour information using histogram specification operation.A new method is proposed to obtain the textural feature of each pixel named as gray level pixel count matrix (GLPCM).The GLPCM method is compared with existing statistical technique i.e. gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM).The classification operation is performed using BPNN classifier.The result of proposed feature extraction technique has been validated based on the ground truth details provided in the dataset and achieved specificity and sensitivity 98.9%, 90.6% on DIARETDB0, DIARETDB1 and DRIVE dataset.In this study also compared different segmentation technique on similar dataset.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".