Deep Learning Architectures for Abnormality Detection in Endoscopy Videos
Bibliographic record
Abstract
Endoscopy is a widely employed technique for the diagnosis and treatment of various internal organs in the human body, including the gastrointestinal tract, lungs, bones, and abdominal region.During the procedure, an illuminated optical device records video data, which assists physicians during real-time analysis and post-procedure evaluations.Identifying areas of interest within the vast amount of recorded video data is critical for optimizing physicians' focus and time.A key task in this process involves classifying endoscopic frames as normal or abnormal.Current solutions for endoscopic frame classification either rely solely on handcrafted features or neural network features and lack efficient pre-processing techniques to eliminate irrelevant frame portions or enhance relevant region features.This study presents an innovative architecture pipeline for the efficient and robust detection of abnormal frames in endoscopic videos, combining effective pre-processing techniques with deep neural networks.A novel and customized pre-processing method has been integrated into three custom-tailored deep architectural pipelines, which are based on sequential convolutional networks, InceptionResNet, and EfficientNet.Models generated using these pipelines were trained and tested on customcurated data from publicly available repositories.Among the three pipelines, the architecture based on EfficientNet outperformed current state-of-the-art approaches, achieving a sensitivity, specificity, and accuracy of 0.94, 0.91, and 0.93, respectively, for the classification of abnormal frames.This novel approach demonstrates the potential of leveraging advanced deep learning architectures to enhance abnormality detection in endoscopic videos.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".