Computer aided detection of bleeding in capsule endoscopy images
Bibliographic record
Abstract
The capsule endoscopy (CE) has been widely used to diagnose the diseases in human digestive tract because of its great breakthrough that it can view the entire small bowel without invasiveness. However, a tough problem associated with this new technology is that too many images to be examined by eyes cause a huge burden to physicians, so it is very useful to help the physician do diagnosis using computerized methods. In this paper, a new method aimed for bleeding region detection in CE images is proposed. This new approach mainly focuses on color texture feature, also a very important clue for the physicians to judge the status of the gastrointestinal tract. We propose a new idea of chromaticity moment as the color feature, which make full use of the Tchebichef polynomials and the illumination invariant of the HSI color space. Then, combined with the uniform local binary pattern (LBP), a traditional texture representation model, it can be used to discriminate normal regions and bleeding regions. Classification of bleeding regions with multilayer perceptron neural network is then deployed to verify the performance of the proposed new color texture features. Experimental results on our present bleeding image data sets show that this new scheme is promising in detecting bleeding regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".