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Proceedings Paper

Hybrid unsupervised-supervised approach for computerized classification of malignant and benign masses on mammograms
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Paper Abstract

A hybrid classifier which combines an unsupervised adaptive resonance network (ARTs) and a supervised linear discriminant classifier (LDA) was developed for analysis of mammographic masses. Initially the ART2 network separates the masses into different classes based on the similarity of the input feature vectors. The resulting classes are subsequently divided into two groups: (1) classes containing only malignant masses and (2) classes containing both malignant and benign or only benign masses. All masses belonging to the second group are used to formulate a single LDA model to classify them as malignant and benign. In this approach, the ART2 network identifies the highly suspicious malignant cases and removes them from a training set, thereby facilitating the formulation of the LDA model. In order to examine the utility of this approach, a data set of 348 regions of interest (ROIs) containing biopsy-proven masses (169 benign and 179 malignant) were used. Ten different partitions of training and test groups were randomly generated using 73% of ROIs for training and 27% for testing. Classifier design including feature selection and weight optimization was performed with the training group. The test group was kept independent of the training group. The performance of the hybrid classifier was compared to that of an LDA classifier alone. Receiver Operating Characteristics (ROC) analysis was used to evaluate the accuracy of the classifier. The average area under the ROC curve (Az) for the hybrid classifier was 0.81 as compared to 0.78 for LDA. The Az values for the partial areas above a true positive fraction of 0.9 were 0.34 and 0.27 for the hybrid and the LDA classifier, respectively. These results indicate that the hybrid classifier is a promising approach for improving the accuracy of classification in CAD applications.

Paper Details

Date Published: 21 May 1999
PDF: 10 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348601
Show Author Affiliations
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Nicholas Petrick, Univ. of Michigan (United States)
Mark A. Helvie, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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