Small bowel image classification using dual tree complex wavelet-based cross co-occurrence features and canonical discriminant analysis
Bibliographic record
Abstract
In this paper, a modified algorithm is proposed for automatically classifying small bowel images into normal or abnormal class. Instead of the shift-invariant overcomplete wavelet transform, our modification performs the dual tree complex transform (DTCWT) to the small bowel images for three-decomposition scales and clamp and linearly scale the DTCWT subbands. As the original algorithm, we extracts cross co-occurrence matrix from each DTCWT subband, and calculate four textural features from each cross co-occurrence matrix. Unlike the original algorithm, we select a subset of the calculated texture features by means of minimum redundancy maximum relevance (mRMR) algorithm. We use canonical discriminant analysis as a classifier in order to classify a small bowel image into normal or abnormal class, just like the original algorithm. Experimental results show that our proposed modification outperforms the original algorithm for small bowel image classification by 5.3% in terms of correct classification rate for the same dataset.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".