Mammographic Masses Descriptor for Breast Cancer Classification and Automatic Diagnosis
Bibliographic record
Abstract
An automatic breast cancer diagnosis is a challenging task because breast masses have a random appearance and vary in size and shape.A descriptor is an algorithm that quantifies elementary characteristics such as color, texture, contour, or shape.In digital mammography, numerous descriptors have been employed to differentiate between benign and malignant tumor patterns, but automatic diagnosis remains a difficult function.In this paper, we proposed a novel approach based on local features to describe masses in mammograms via Polygon Approximation Triangle-Area Representation (PATAR).As the degree of spiculation in masse defines their level of malignancy, the strength of our approach lies in its ability to isolate and measure spiculations in breast masses.PATAR is a robust image descriptor composed of two steps: polygon approximation and triangle-area representation.Firstly, we applied a polygon approximation to the masses to raise the most critical spiculations and lobulations.Then, by browsing the points of the polygon, calculate the triangle's area formed by the vertices of the polygon, ears, and mouths.The extracted characteristics describe the shape and show the severity of spiculations with high precision.Digital mammography CBIS-DDSM is used to evaluate the method with a Fuzzy C-Means classifier, Support Vector Machines (SVM), and Random Forest (RF).The Random Forest classifier achieved the best performance, reaching 97,94%.The proposed method provides a fully automated diagnosis with the best accuracy and invariance to scale and rotation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".