The study is to investigate the use of a Bayesian belief network (BBN) in a computer-assisted diagnosis (CAD) scheme for mass detection in digitized mammograms. Two independent image sets were used in the experiments. After initial processing of image segmentation and adaptive topographic region growth in our CAD scheme, 288 true-positive mass regions and 2,204 false-positive regions were identified in the training image set. In the testing set, 304 true-positive and 1,586 false-positive regions were identified. Fifty features were computed for each region. After using a genetic algorithm search, a BBN was constructed based on 12 local and four global features in order to classify these regions as positive or negative for mass. The performance of the BBN was evaluated using an ROC methodology. The BBN achieved an area under the ROC curve of 0.873 plus or minus 0.009 in classifying the 304 positive and 1,586 negative regions in the testing set. This result was better than using an artificial neural network with the same set of input features. After incorporating the BBN into our CAD scheme as the last classification stage, we detected 80% of 189 positive mass cases (in 433 testing images) with an average detection rate of 0.76 false-positive regions per image. Therefore, this study demonstrated that a BBN approach could yield a comparable performance to that using other classifiers. Using a probabilistic learning concept and interpretable topology, the BBN provides a flexible approach to improving CAD schemes.