Learned fuzzy rules versus decision trees in classifying microcalcifications in mammograms
Lawrence Hall · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
Screening mammograms for microcalcifications is important labor intensive work for an expert physician. A fatigued or inexperienced person might miss an abnormal mammogram, which is why the practice of having two readers for mammograms is not uncommon. A set of 63 features extracted from 40 mammograms, each with ground truthed microcalcifications, are used for learning and testing a set of rules to classify pixels as microcalcification or normal. A decision tree is used to learn these rules. Results from applying the rules to unseen mammograms are discussed. We also discuss a method of fuzzifying the decision tree which should lead to improved classification accuracy.