Mammography Image Segmentation Using Combined Fuzzy Logic and Neural Networks for Breast cancer Detection
Bibliographic record
Abstract
Breast cancer is one of the major causes of death and the most common type of cancer among women worldwide. Breast cancer risk increases with age. Women within the age of 15-54 have more risk of breast cancer. Treatment is more efficient when detected early, as the evolution into a more severe stage is avoided. Screening mammography has been recommended as the most effective method for early detection of breast cancer. Computed aided detection intends to provide assistance to the mammography detection, reducing breast cancer misdiagnosis, and consequently allowing better treatment and prognosis. Several research works have tried to develop computer aided diagnosis tools. They could help radiologists interpret mammograms and could be useful for an accurate diagnosis. The purpose of this study is to introduce a new method based on the combination of neural networks and fuzzy logic in order for better detection and prognosis of breast cancer in mammography images. Results show that the proposed method is better than previous works and can facilitate the doctor to detect breast cancer in early stages of diagnosis process.
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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) | 0.000 | 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".