Analytical and experimental solution for heat source located under skin: chest tumor detection via IR camera
Bibliographic record
Abstract
Infrared (IR) imaging could be used as both noninvasive and nonionizing technology. Utilizing IR camera, it is possible to measure skin temperature with the aim of finding any superficial tumors. Since tumors are highly vascular and usually have a higher temperature than the rest of the body, using thermograms, it is possible to assess various tumor parameters, such as depth, intensity, and radius. In this study, we have developed an analytical method to detect tumor parameters in both spherical and cubical tissues to represent female breast and male chest tissue. This includes development of analytical solution for solving inverse bio-heat problem as well as laboratory set up for further validation of the analytical achievements. The models were developed by solving Penne’s Bioheat equation for each tissue under certain conditions and two main assumptions: 1. The tumor was assumed as separate heat source; 2. The developed model does not change with time (steady state condition). Finally, the analytical findings were validated by utilizing a laboratory test set-up containing an IR camera, 1% Agar solution (tissue phantom), and a heater of variable powers. The models were set to test by adjusting the heater (0.9W) in various depth and imaging the tissue phantom. Comparing the analytically obtained results with the experimental results, it can be concluded that the method is able to detect superficial tumors of small size only by measuring the body surface temperature and ambient temperature.
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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) | 0.001 | 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 | 0.001 |
| 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".