A Various CAD systems using mammogram for diagnosing breast cancer
Bibliographic record
Abstract
According to the report by Indian Council of Medical Research (ICMR) among women, total number of cancer cases is 7,12,758 in 2020 and may reach 8,06,218 in 2025. Breast Cancer is the mostly occur in women is expected to be 2,38,908 cases in 2025 which is 30% of total cancer causes. Detection of breast cancer at the initial stage is a main role in dropping the cancer cases. Developing a Computer Aided Diagnosis (CAD) system helps the radiologist to diagnosis the cancer at the initial stage. In this study review of recent CAD advancements, overview of steps used, methods used in each step is done. The study reveals that even though CAD system is promising but the current performance level could be improved. The performance level can be increased by improving the existing methods or finding new methods, so that CAD can be used as standalone system for detection and diagnosing breast cancer at clinics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".