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14 June 1996 Learned fuzzy rules versus decision trees in classifying microcalcifications in mammograms
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Abstract
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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lawrence O. Hall "Learned fuzzy rules versus decision trees in classifying microcalcifications in mammograms", Proc. SPIE 2761, Applications of Fuzzy Logic Technology III, (14 June 1996); https://doi.org/10.1117/12.243265
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