Breast tumour identification based on inverse scattering approach
Bibliographic record
Abstract
This study introduces an iterative evolutionary method in detection of breast tumour, identification of its size and location. At primary measurement step, a set of antennas capable of transmitting wide‐band Gaussian pulses, generated through the chirping technique, is placed around the breast and total wave received by each directional antenna is recorded in a bistatic manner for every cross section of the breast corresponding to a specific height for a full three‐dimensional scan. Each set of data for each cross section is then analysed separately. Measured fields are used in an inverse‐scattering problem, where the unknown is the tumour. To solve this problem, the finite‐difference time‐domain technique produces the total field resulted from the same incident wave as in measurement while illuminating an electromagnetic model of the breast, each time having a new tumour specification as a part of an evolutionary algorithm scheme. A comparison between various generated and measured reference data is repeated to reach a minimum cost function that corresponds to a minimum error in identification. Particle swarm optimisation and differential evolution are the two optimisations, each using all field elements of both TE z and TM z modes to reach convergence. The final results show an accurate identification of the tumour by this method.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".