An automatic bleeding detection scheme in wireless capsule endoscopy based on statistical features in hue space
Bibliographic record
Abstract
Wireless capsule endoscopy (WCE) is a recently developed video technology to detect small intestine diseases, such as bleeding. For analyzing WCE video frames, instead of using the most common RGB (red, green, blue) color scheme, in this paper, HSV (hue, saturation, intensity value) color scheme is used, which corresponds better to human perception system. The HSV color scheme exhibits less sensitivity to illumination changes, which helps in handling the problem of illumination variation in WCE videos due to the weakening of battery. Different statistical features computed from H, S, and V spaces of WCE images are investigated and it is found that hue provides a useful feature as it captures intrinsic information about the color of objects or surfaces in a scene. Hence in this paper, an automatic bleeding detection scheme from WCE video is proposed utilizing the hue space. Among different statistical measures, mean, standard deviation, variance and moment exhibit significantly distinguishable characteristics for bleeding and non-bleeding images. For the purpose of classification, K-nearest neighbor (KNN) classifier is employed. From extensive experimentation on several WCE videos collected from a publicly available database, it is observed that the bleeding detection performance of the proposed method in terms of accuracy, sensitivity and specificity is quite satisfactory in comparison to that obtained by some of the existing methods.
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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) | 0.000 | 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) | 0.000 | 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 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".