Early Detection of Breast Cancer using Diffuse Optical Probe and Ensemble Learning Method

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.