An application of the empirical mode decomposition to brain magnetic resonance images classification
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Bibliographic record
Abstract
A new approach to distinguish normal from abnormal brain magnetic resonance (MR) images is presented. First, the empirical mode decomposition (EMD) is applied to brain MR images to obtain high frequency intrinsic mode functions (IMF) from which features are extracted. Then, an entropy-based selection process is used to identify the most informative and non redundant features from each IMF before classification by support vector machines (SVM). The validation of the approach with a MR image database consisting of Alzheimer's disease, glioma, herpes encephalitis, metastatic bronchogenic carcinoma, multiple sclerosis, and normal condition shows its effectiveness as well as slightly better classification efficiency in comparison to using discrete wavelet transform-based alternatives. However, the EMD approach is substantially more time consuming.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it