Early Detection of Breast Cancer using Diffuse Optical Probe and Ensemble Learning Method
Bibliographic record
Abstract
In this paper, we propose using the diffuse optical breast scanning (DOB-Scan) probe, which employs an ensemble learning method to enable earlier detection of breast cancer. For this, we utilized an ensemble of nine models with various regression algorithms as base estimators to predict optical properties for liquid breast-mimicking phantoms. These regression models included Polynomial Regression, Support Vector, Random Forest, K-Nearest Neighbors, Decision Tree, Multi-layer Perceptron, XGBoost, CatBoost, and Extra Trees Regressors. We evaluated the performance of our models based on accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). Our analysis revealed that the Extra Trees model had the highest accuracy of 93%, making it the best regression model. Additionally, the Bagging with the KNN model achieved 100% accuracy in classifying the optical properties into healthy and unhealthy categories. These results suggest that the DOB-Scan probe, utilizing an ensemble learning approach, has the potential to detect breast cancer at an earlier stage.
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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) | 0.000 | 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 | 0.000 |
| 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".