Detection of breast tumor with a frequency selective surface loaded ultra-wide band antenna system
Bibliographic record
Abstract
Abstract Breast tumors are a significant cause to the global death rate among women. However, the fatality rate can be lowered through early detection. This paper presents an ultra-wideband, modified patch antenna of a compact size that can be used for microwave-sensing biomedical applications in the detection of breast cancer. A partial ground plane and slots are implemented in a transformed patch antenna to enhance the impedance bandwidth. The antenna is backed by a uniform frequency selective surface of 5 × 5 unit cells to achieve the necessary antenna characteristics, specifically directivity and gain, for microwave detection applications. Through optimization and fabrication, the final design maintained (|S11|< −10 dB) over the entire frequency band of 11.6 GHz (3.1–14.7 GHz) and achieved an average gain of over 5 dBi. Other metrics, such as group delay and the fidelity factor in different setups, are also simulated to observe the expected performance in the required frequency range. Finally, based on simulation, a model is suggested that comprises various configurations of antenna arrays, including one Tx antenna and one to seven Rx antennas. Further, breast phantom with different tumor sizes and locations were used in the simulation. The simulation results successfully validated the detection of breast cancer cells. We believe these technologies can open possibilities in healthcare applications for identifying tumors.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".