Development of methodology for detection of abnormal functioning region in human external organs using thermogram analysis
Bibliographic record
Abstract
This study has established thermography as a potential diagnostic tool for detecting and parameterizing tumors even at the earlier stage from abnormal local surface thermal features captured by high sensitive infrared cameras without known risk of morbidity. Discrepant thermal features originate not only for tumor’s distinguishing physio-bio-thermal features but also for the state of health, resulting in thermography as a useful tool for retrieving information about the tissue’s health, thus an efficient screening tool. Accurate linking between hyper functional tissues and thermal pattern could turn the screening tool into a promising parameterizing tool. Human external organs, for example chest, forearm and breast have been modeled, mimicking their shapes, inhomogeneity and deformations to obtain steady-state thermal feature in the tissue interior at healthy state and the computation is extended for tumors buried into healthy tissues for determining abnormal local surface thermal image. Tumor diagnosis parameters have been forecasted from thermogram using an indirect process involving the optimization process. The study has applied gradient (gradient projection method), non-gradient (pattern search method) and learning based (genetic algorithm) optimization approaches. Feasibility of the proposed technique is investigated for tumors in human organs. The local abnormal thermal feature screens earlier stage tumors out and reveal how tumors affect the thermal behaviour and what particular parameters have significant influence on the thermal image. Influential parameters are applied as optimization variables and their influences are also figured out to determine the gradient matrix for the gradient optimization technique. The study has employed bio-heat equations, heat-source model and Artificial Neural Network as governing equation to develop simulated datasets. The simulated dataset is compared with test thermogram to minimize a cost function. In lieu of clinical thermograms, the study has developed pretend thermogram with enveloping the simulated datasets with ±10% random noise. This research has tailored optimization algorithms for estimating tumor depth, size, blood perfusion rate, thermal conductivity, and metabolism and the obtained results show good accuracy. The estimated parameters are given to a trained network to reconstruct the thermal feature, thus, validates the performance of the proposed methodology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".