Brain tumour segmentation using weighted k-means based on particle swarm optimisation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In medical science, image segmentation (IS) is a challenging task, it subdivides the image into mutually exclusive regions. An IS is the most fundamental and essential process of classification, description and visualisation of the region of interest in several medical images. In the medical field, diagnosis of brain and other medical images are using magnetic resonance imaging (MRI), which is a very helpful diagnostic tool. The traditional technique using MRI brain tumour segmentation (BTS) is extremely time consuming task. This research paper concentrates on the improved medical IS method based on hybrid clustering methods. This hybrid technique is a combination of weighted k-means and fuzzy C-means (WKFCM), K-means and particle swarm optimisation (KPSO). The proposed techniques, identify the brain tumour accurately with less execution time. An experimental result demonstrated that proposed hybrid clustering technique performance is better than the earlier methods like FCM, KM, mean shift (MS), expectation maximisation, and PSO in three different benchmark brain databases.
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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 | 0.001 |
| 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 | 0.001 |
| 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