Detection of Tumours Using Breast Surface Thermal Patterns
Bibliographic record
Abstract
Influencing factors are important considerations in the application of non-invasive thermal diagnostics for the early detection of breast tumour. In this paper, experimental studies of artificial tumours embedded inside silicone breasts coupled with numerical simulations using 3D finite-element method in ANSYS were used to investigate the effects of tissue conductivity, tumour size and tumour depth on the heat patterns at the breast skin surface. After validating the numerical breast model, the analysis was extended to examine the heat patterns of a growing tumour. The findings revealed that thermal patterns of the breast surface over time could be useful for the detection of tumours. The existence of tumours would be more noticeable from thermal images of breasts with more fatty tissues, and breasts of lower density. The method would be more suitable for the detection of large tumours near the skin surface. The simulated results suggested that it is possible to detect an initial 4 mm HER2-positive tumour at a depth of 44 mm after about 196 days when it had grown to 7 mm using a temperature sensor with resolution of 0.01 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".