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Proceedings Paper

Learned fuzzy rules versus decision trees in classifying microcalcifications in mammograms
Author(s): Lawrence O. Hall
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Paper 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.

Paper Details

Date Published: 14 June 1996
PDF: 8 pages
Proc. SPIE 2761, Applications of Fuzzy Logic Technology III, (14 June 1996); doi: 10.1117/12.243265
Show Author Affiliations
Lawrence O. Hall, Univ. of South Florida (United States)


Published in SPIE Proceedings Vol. 2761:
Applications of Fuzzy Logic Technology III
Bruno Bosacchi; James C. Bezdek, Editor(s)

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