A Hybrid Method for Multiple Sclerosis Lesion Segmentation Using Wavelet and Dense U-Net
Bibliographic record
Abstract
Multiple Sclerosis (MS) is one of the debilitating disorders of the central nervous system. This disease causes lesions in the white matter of the brain tissue. It can also lead to many physical and psychological disorders in movement, vision, and memory. Lesion segmentation in MRI images to determine the number and size of lesions is one of the diagnostic problems for specialists. Using automated diagnostic tools as an aid can help professionals. Traditional image processing and deep learning methods are used to automate lesion segmentation. The U-Net is one of the most widely used deep learning architectures for MS lesion segmentation. The images are used in the Fourier domain in the U-Net network, which does not include all its features. Our proposed method combines the HAR wavelet transform and the Dense net-based U-Net. This makes local features and lesions of different sizes more prominent and leads to higher quality segmentation. The proposed method had a better Dice value than the compared methods in the experiments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".