Enhanced Detection of Colorectal Polyps in Endoscopy: A Comparative Analysis Using YOLOv8 and YOLOv9 Models
Bibliographic record
Abstract
Polyps are abnormal tissue growths that occur in various organs, with notable prevalence in the gastrointestinal tract. The main concern is the development of polyps in the colon and rectum because they are important precursors for colorectal cancer (CRC). Statistics show that CRC is the third most common diagnosed cancer in the United States and the second leading cause of cancer deaths. The best prevention method is to regularly get a colorectal cancer screening through a procedure called a colonoscopy to find and remove polyps early on. The utilization of artificial intelligence to automatically detect polyps have become common and can significantly reduce the misdiagnosis of CRC. Hence, we propose to use the industry leading object detection model, YOLO. This paper investigates the potential of YOLOv9, the latest iteration of the YOLO model, for automatic polyp detection. We perform a comparative analysis of YOLOv8 and YOLOv9 for polyps detection, evaluating their performance on a combined dataset comprised of images and labels from Kvasir-SEG and SUN Colonoscopy Video Database.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".