Fractal dimension and high order statistics of spectral energy distribution as features for pathology detection in brain MR images
Bibliographic record
Abstract
A new methodology to detect pathologies in human brain magnetic resonance (MR) images is investigated. It is based on edge extraction in the Hilbert domain and subsequent analysis by means of fractal dimension and spectral energy distribution high order statistics. The technique is particularly suitable for pathologies characterized by bright structures in the MR images as do Glioma and Metastatic bronchogenic carcinoma. When classifying normal versus abnormal images dues to these two pathologies, ANOVA statistics show that the suggested features have strong between-classes differences, and the obtained classification accuracy by support vector machines is 99.9%±0.006. In comparison, applying a standard feature extraction technique based on the discrete wavelet transform (DWT) and principal component analysis (PCA) yielded 85.2%±0.05 accuracy.
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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 | 0.000 | 0.001 |
| 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 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".