Multi-Variate and Multi-dimensional CFAR Detection of Breast Cancer
Bibliographic record
Abstract
Abstract Breast cancer is the most common type of cancer in females. In many cases, the mortality rate can be drastically lowered if the disease is detected early. Due to its safety and lack of risk to the patient, microwave breast imaging is considered a potential replacement for mammography. This paper presents a breast cancer detection approach based on the Multi-Variate and Multi-Dimensional Constant False Alarm Rate (MVMD-CFAR) method. This method has several advantages over mammography using x-rays, including increased patient comfort and lower costs. On an open-source experimental database derived from the University of Manitoba Microwave Mammography Dataset UM-BMID, the performance of the (2D-CFAR) method is evaluated by examining the available data set for breast microwave sensing. We segregate infected and healthy samples and assessed the probability density function PDF for pictures of normal and malignant tissue. The third dimension of the algorithm is the image's color data, which comprises three variables (three colors). Initial testing show that the MVMD-CFAR detector is highly effective, with a detection probability of 97.4% and a false alarm probability of 10%. However, a few challenges must be overcome before this imaging technique can reach its full potential and be implemented in clinical settings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".