Bayesian Dynamic Ensemble Selection Method for Dynamic Optical Breast Imaging Classification

Abstract

Breast cancer poses a significant threat to women's health. Dynamic Optical Breast Imaging (DOBI) is a medical imaging method based on the theory of early neovascularization in breast tumors. This technique is fast, non-invasive, and radiation-free, with the potential for early diagnosis of breast cancer, thereby helping to enhance patients’ survival rate and treatment outcomes. However, due to limitations such as limited data volume and class imbalance, existing medical image classification methods often suffer from low classification accuracy, poor generalization ability, and low sensitivity to malignant samples when applied to DOBI. To address these issues, this paper proposes the Bayesian Dynamic Ensemble Selection (BDES) method. In the BDES method, the K-Nearest Neighbor Dynamic Classifier Selection (KNND-CS) method is designed to construct specific classifiers pool based on all available base classifiers for each test sample. Subsequently, the simulated annealing algorithm is utilized to dynamically select classifiers from this pool for inclusion in the ensemble. Finally, the selected classifiers are ensembled by Bayesian probability fusion function to generate the final diagnosis result of benign or malignant breast tumors. The BDES method dynamically selects and integrates appropriate classifiers for each sample, enhancing DOBI's accuracy in diagnosing benign and malignant breast tumors while ensuring robustness and generalization. To validate the effectiveness of BDES, extensive experiments were conducted. Cross-validation experiment proved the generalization and robustness of the DBES method. And the comparation experiment in breast cancer diagnosing for the DOBI dataset shows that the accuracy and sensitivity of the BDES method are 83% and 78% respectively, which is significantly better than many comparative methods, proving the effectiveness of the new method in early diagnosis of breast cancer.