Improving the Diagnostic Capability of Microwave Radar Imaging Systems using Machine Learning
Bibliographic record
Abstract
Breast microwave sensing (BMS) is a potential breast cancer detection technique that uses low-power microwave radiation to detect the presence of cancerous lesions. This work presents the results of the application of a multilayer perceptron (MLP) and support vector machine with radial basis function (SVM RBF) to breast cancer detection for a portable BMS prototype. Numerical 2D phantoms belonging to either BI-RADS Class 1 or Class 2 classifications were used to produce simulated data as collected by the portable system using an array of twelve sensors operating at five frequencies between 2.3 GHz and 6.5 GHz. Five feature preprocessing pipelines and their impact on classification performance were evaluated. An area under the curve of the receiver operating curve (ROC AUC) as high as (95 ± 1)% for BI-RADS Class 1 and as high as (94 ± 1)% for BI-RADS Class 2 were obtained using the SVM RBF, and as high as (94 ± 1)% for Class 1 and (92 ± 2)% for Class 2 using the MLP.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".