Although the computer-aided diagnosis (CAD) system can be applied for classifying the breast masses, the
effects of this method on improvement of the radiologist’ accuracy for distinguishing malignant from benign
lesions still remain unclear. This study provided a novel method to classify breast masses by integrating the
intelligence of human and machine. In this research, 224 breast masses were selected in mammography from
database of DDSM with Breast Imaging Reporting and Data System (BI-RADS) categories. Three observers
(a senior and a junior radiologist, as well as a radiology resident) were employed to independently read and
classify these masses utilizing the Positive Predictive Values (PPV) for each BI-RADS category. Meanwhile,
a CAD system was also implemented for classification of these breast masses between malignant and benign.
To combine the decisions from the radiologists and CAD, the fusion method of the Multi-Agent was provided.
Significant improvements are observed for the fusion system over solely radiologist or CAD. The area under
the receiver operating characteristic curve (AUC) of the fusion system increased by 9.6%, 10.3% and 21%
compared to that of radiologists with senior, junior and resident level, respectively. In addition, the AUC of
this method based on the fusion of each radiologist and CAD are 3.5%, 3.6% and 3.3% higher than that of
CAD alone. Finally, the fusion of the three radiologists with CAD achieved AUC value of 0.957, which was
5.6% larger compared to CAD. Our results indicated that the proposed fusion method has better performance
than radiologist or CAD alone.