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The structure and parameters of a belief network are learned in order to categorize cytogenetic images enabling the detection of genetic syndromes. We compare a structure learned from the data to another obtained utilizing expert knowledge and to the naive Bayesian classifier. We also study feature quantization needed for parameter learning in comparison to density estimation. Both networks achieve comparable accuracy for the cytogenetic database with a slight advantage to that based on expert knowledge.