Near Field Breast Tumor Detection Using Ultra-Narrow Band Probe with Machine Learning Techniques
Bibliographic record
Abstract
In this work, we propose a near-field microwave sensing modality that uses a single probe combined with a classification algorithm to help in detecting the presence of tumors in the human female breast. The concept employs a near-field resonant probe with an ultra-narrow frequency response. The resonant probe is highly sensitive to the changes in the electromagnetic properties of the breast tissues such that the presence of the tumor is gauged by determining the changes in the magnitude and phase response of the sensor's reflection coefficient. A key feature of our proposed detection concept is the simultaneous sensing of tissue property changes to the two female breasts since the right and left healthy breasts are morphologically and materially identical. Once the probe response is recorded for both breasts, the Principle Component Analysis (PCA) method is employed to emphasize the difference in the probe responses. For validation of the concept, tumors embedded in a realistic breast phantoms were simulated. To provide additional confidence in the detection modality introduced here, experimental results of three different sizes of metallic spheres, mimicking tumors, inserted inside chicken and beef meat were detected using an electrically-small probe operating at 200 MHz. The results obtained from the numerical tests and experiments strongly suggest that the detection modality presented here might lead to an inexpensive and portable early and regular screening for breast tumor.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".