Improved Detection of Abnormality in Grayscale Breast Thermal Images Using Binary Encoding
Bibliographic record
Abstract
Breast cancer is one of the most frequently diagnosed cancers among women. Thermography is an adjunct tool to mammography that facilitates early abnormality detection. The temperature matrices recorded during thermography are pseudo-colored to form RGB images using user-defined color palettes and these color palettes must be chosen cautiously for accurate abnormality detection. Grayscale breast thermal images linearly mapped from temperature matrices are independent of the choice of user-defined color palettes; however, the absence of distinct gradients of tumors in grayscale breast thermal images deteriorates the performance of abnormality detection. Therefore, this work introduces the concept of gradient enhancement of grayscale breast thermal images using local non-parametric binary encoding transforms, namely, census transform (CT) and local binary pattern (LBP) for enhanced abnormality detection. A comparative analysis of the performance of abnormality detection using different types of features (statistical and textural) with binary encoded breast thermal images as inputs is presented. Breast thermal images encoded using CT achieve higher abnormality detection as compared to breast thermal images encoded using LBP. The proposed methodology achieves a balanced accuracy of 94.26% with CT-transformed breast thermal images which is approximately 6% higher than the balanced accuracy achieved by non-binary encoded grayscale breast thermal images when textural features were employed for abnormality detection. The influence of the number of neighboring pixels of the CT and LBP transforms on the performance of abnormality detection is also investigated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".