Alzheimer’s Disease MRI Brain Segmentation Using Pythagorean Fuzzy Sets
Bibliographic record
Abstract
Alzheimer's disease (AD) is a degenerative and ultimately fatal brain disorder for which there is no cure.This neurological condition, with a complex etiology, causes dementia and cognitive decline, making its identification challenging due to the variation in brain MRIs, including differences in size, shape, and CSF flow.While there is no treatment for AD, its progression can be slowed with early diagnosis.Many researchers have employed image processing-based techniques to differentiate between normal and AD-affected patients based on brain images.However, the brain's regions often look super similar, making it tricky to pinpoint specific areas, plus there's always some uncertainty when it comes to extracting the exact regions.There have been various proposals in the literature for fuzzy cmeans and intuitionistic fuzzy c-means (IFCM) approaches to deal with this ambiguity and uncertainty.In contrast, Pythagorean fuzzy sets (PFS) provide a more precise means of verifying membership, making them an effective tool for managing uncertainty.The author analyzed PFS and applied fuzzy c-means to propose Pythagorean fuzzy c-means (PFCM).Additionally, histogram-based initial centroids were used to avoid the local minima problem, which is common in many clustering algorithms.The proposed clustering algorithm demonstrated improved performance, completing execution in less than 1.5 seconds, owing to the incorporation of initial centroids and PFS-based clustering.The proposed method achieved high accuracy rates: 98.64% for white matter (WM), 97.4% for gray matter (GM), and 98.14% for cerebrospinal fluid (CSF) in detecting brain tissues.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".