A New Method Based on Convolutional Neural Networks and Discrete Wavelet Transform for Detection, Classification and Tracking of Colon Polyps in Colonoscopy Videos
Bibliographic record
Abstract
In this study, a new method based on Convolutional Neural Network (CNN), Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) is presented for polyp detection, classification and tracking during colonoscopy.The proposed method is constructed in 3 parts.1) Detection of polyps with deep learning based Faster R-CNN for detection of polyps 2) Classification of detected polyps by CNN-DWT-SVM.3) Tracking for polyps counting.The proposed method was trained and tested with the Colonoscopy Dataset, a public data set.In the first step of the method, polyp detection was carried out with pre-trained ResNet 50 CNN architecture with 92.6% precision.The regions identified in the second step of the method were classified for four classes adenoma, hyperplastic, lumen, serrated and 94.7% classification accuracy was obtained.With the proposed method, the detection sensitivity of Faster R-CNN was increased from 92.6% to 99.2%, and the accuracy of 95.4% was achieved by using DWT in the classification of polyp classes.In the classification process, 98% correct adenoma, 95% hyperplastic, 90% luminal intestine, 96% serrated polyp were reached.The proposed method reached an average of 94% MOTA in polyp tracking and was able to detect polyp frames with their classes with 99.2% precision.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".