Bilateral Symmetry-Based Abnormality Detection in Breast Thermograms Using Textural Features of Hot Regions
Bibliographic record
Abstract
With an increase in the number of breast cancer cases worldwide, there is an urgent need to develop techniques for early abnormality detection. Thermography is known for its potential to detect breast abnormalities at an early stage. A novel threshold-based non-machine learning asymmetry analysis using textural features is proposed for breast abnormality detection. Breast abnormalities are indicated by regions of elevated temperatures (hot-regions), usually, indicated by red colour in thermograms. In this work, the breast thermograms are segmented to extract breast tissue profiles and then the red-plane of an RGB thermogram is utilized to analyze the natural contralateral symmetry between the left and right breast of an individual. A novel textural feature based on histogram similarity along with known textural features such as fractal dimension, hurst exponent, spectral norm and Frobenius norm are used as features for asymmetry analysis. Bilateral ratios of these features indicate contralateral symmetry between the left and right breast. A bilateral ratio value closer to 1 indicates such symmetry. Hard voting is done among all the bilateral ratios of the textural features to estimate asymmetry between the left and right breast and detect an individual with breast abnormality. The proposed methodology is evaluated on publicly available datasets. It outperforms the state-of-the-art and achieves an accuracy of 96.08%, sensitivity of 100% and specificity of 93.57%. A comparative analysis of statistical and textural features has also been demonstrated. A novel singular value decomposition (SVD)-based abnormal breast detection technique has been proposed with evaluations on a limited dataset.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".