Development and Characterization of Skin Phantoms at Microwave Frequencies
Bibliographic record
Abstract
Realistic tissue-mimicking phantoms are required for experimental evaluation and validation of microwave reflectometry prototype systems for skin cancer detection before performing any tests on human subjects. These phantoms must accurately emulate the dielectric properties for both healthy and malignant skin tissues. In this work, we develop and experimentally investigate multiple skin phantoms with tumor inclusions in the frequency range of 0.5–26.5 GHz. These heterogeneous phantoms are realized by varying the tumor size and placement relative to the skin. The tumors with irregular borders are also investigated. For analyzing the effect of underlying skin on dielectric properties, two skin thicknesses are considered: 8 mm and 2.5 mm. The proposed heterogeneous phantoms are developed using inexpensive materials: oil, gelatin, deionized water and formaldehyde. The dielectric properties of fabricated phantoms are characterized with Keysight performance probe connected with a FieldFox handheld vector network analyzer. Our results demonstrate that the dielectric properties of the developed phantoms closely agree with those of the excised malignant human tissues reported in the literature over the entire frequency range of 0.5–26.5 GHz and can be hence reliably used for experimental validation in studies towards microwave-based diagnostics of skin lesions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
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".