MRI Brain Image Segmentation Using Combined Fuzzy Logic and Neural Networks for Tumor Detection
Bibliographic record
Abstract
Considering that brain tumor is one of the diseases which threaten members of a society and unless it is not diagnosed at the right time it can lead to people’s death, its diagnosis is of too much importance. In most cases individual develops tumor lesion but since it is very small, it cannot be detected by first medical images such as CT and MRI and it may postpone diagnosis and may also lead to an irreparable lesion. During the past decade in order to help radiologists and specialized physicians, most experts have tended to pay more attention to computer algorithms for the diagnosis of this phenomenon. In this case they can use computer to analyze medical images taken from brain more precisely and tumor detection can be done. Using this method may lead to reduce the risk of tumor diagnosis. In this article we extract candidate abnormal areas by the use of morphological operations and then combination of artificial neural networks and fuzzy logic that refers to NeuroFuzzy is used to classify tumor region from non tumor candidate areas. After localization of the tumor region Whole brain tumor boundary was extracted by the use of traditional level set method. The evaluation result with brain MRI tumor images shows that our proposed method is more precise and robust for brain tumor segmentation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".