Automatic detection of Alzheimer disease in brain magnetic resonance images using fractal features
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes a fractal-based processing methodology to detect Alzheimer's disease (AD) in brain magnetic resonance images (MRI). The proposed diagnosis system does not require image preprocessing and segmentation, leading to a simple implementation. The brain MRI is transformed first into a one-dimensional (1-D) signal for faster processing. Then, a three-component feature vector is extracted to characterize the 1-D signal's local and global fractal features. The features include Hurst's exponent and two results from detrended fluctuation analysis (DFA): the scaling exponent and the total fluctuation energy. The validation with ten normal brain MRIs and thirteen abnormal MRIs corresponding to AD led to 100% classification accuracy using support vector machines with a quadratic kernel. It is concluded that the proposed methodology can be as accurate as the best alternative approach while being simpler to implement.
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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) | 0.003 | 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