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The purpose of this study is to investigate the significance of the multi-agent interactive information fusion algorithm over the matter of identification of breast masses in digitized images. For the lack of enough correlation information between the individual classifiers, the generalization performance of the Bayesian fusion method is sometimes far from the expected level, and thereby the multi-agent fusion method is presented in this paper. The multi-agent interactive information fusion algorithm applies precisely this correlation information to improve the performance, especially the robustness. In this experiment, the presented method is compared with four common fusion algorithms, all of which are applied to the UCI dataset with 569 cases (357 depicting malignant cases and 212 depicting benign cases), and the experimental results show that it does not only circumvent the accuracy and sensitivity need of the breast masses identification in digitized images, but also achieves performance close to the robustness.