Near-Field Microwave Loop Array Sensor for Breast Tumor Detection
Bibliographic record
Abstract
In this work, we propose the use of loop antenna arrays excited by a single port as a microwave sensor for near-field microwave breast tumor detection. The proposed sensor has a high sensitivity to detect an abnormality in the dielectric properties of breast tissues. The existence of the breast tumors is detected by estimating the variations in the response of the reflection coefficient of the sensor for both tumours and healthy cases. The developed sensor comprises 4-element identical loop antenna array fed with a single port, the use of antenna array enlarges the sensitivity area as compared to a single element resulting in better detection of tumors located deeply inside breast tissues. Numerical simulations have been conducted using a numerical breast model with and without tumor cells placed in the near-field of the sensor. The sensor is capable of detecting a breast tumor inserted at five different locations and with various sizes. An experimental validation was conducted using a glass box filled with vegetable oil and metallic spheres that resembles healthy and tumourous breast tissues, respectively. The simulation and experimental results show that the array sensor has a high sensitivity for detecting a metallic sphere placed at five different locations inside a dielectric medium as well as detecting variable sizes of the metallic spheres.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".