Detection of brain tumour by using moments and transforms on segmented magnetic resonance brain images
Bibliographic record
Abstract
Brain tumour occurs when abnormal cells appear within the brain. Primary tumour starts with abnormal growth of brain cells whereas Secondary (Metastatic) tumour initiates as cancer in other parts of the body and spread to the brain through blood stream. In this paper, we propose a novel approach to detect tumour in magnetic resonance (MR) brain images. The proposed method uses improved incremental self organise mapping (I2SOM) to segment the brain image and to calculate asymmetry Zernike moments (ZMs), Pseudo-Zernike moments (PZMs) and orthogonal Fourier Mellin moments (OFMMs) are used. It omits the limitation of pre-determination of class of input data and the manual setting of appropriate threshold value. The effectiveness of the proposed method is analysed by doing experiments on 30 MR brain images with tumour and 30 normal MR brain images. It is observed that tumour detection is successfully realised for 30 MR brain images with tumour.
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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 | 0.001 | 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".