Local relative GLRLM-based texture feature extraction for classifying ultrasound medical images
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Bibliographic record
Abstract
This paper presents a new approach of extracting local relative texture feature from ultrasound medical images using the Gray Level Run Length Matrix (GLRLM) based global feature. To adapt the traditional global approach of GLRLM -based feature extraction method, a three level partitioning of images has been proposed that enables capturing of local features in terms of global image properties. Local relative features are then calculated as the absolute difference of the global features of each lower layer partition sub-block and that of its corresponding upper layer partition block. Performance of the proposed local relative feature extraction method has been verified by applying it in classifying ultrasound medical images of ovarian abnormalities. Besides, significant improvement has been noticed by comparing the proposed method with traditional GLRLM -based feature extraction method in terms of image classification performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it