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Proceedings Paper

Image classification using artifical neural networks
Author(s): Berkman Sahiner; Heang-Ping Chan; Nicholas Petrick; Datong Wei; Mark A. Helvie M.D.; Dorit D. Adler; Mitchell M. Goodsitt
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Paper Abstract

A convolution neural network (CNN) and a backpropagation neural network (BPN) were used for classification of regions of interest (ROIs) on mammograms as either mass or normal tissue. Input images to the CNN were obtained from the ROIs using averaging and subsampling. Input features to the BPN were obtained from spatial gray level dependence (SGLD) matrices at multiple resolutions. A data set consisting of 168 ROIs containing biopsy- proven masses and 504 ROIs containing normal breast tissue was used for training and testing of the neural networks. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. The area under the test ROC curve reached 0.83 for the CNN, 0.88 for the BPN, and 0.89 for a combination of the CNN and BPN outputs. Our results indicate that (1) CNN performance may be improved by using additional texture information; and (2) the overall performance may be improved by combining CNN and BPN classifiers.

Paper Details

Date Published: 12 May 1995
PDF: 8 pages
Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995);
Show Author Affiliations
Berkman Sahiner, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Nicholas Petrick, Univ. of Michigan (United States)
Datong Wei, Univ. of Michigan (United States)
Mark A. Helvie M.D., Univ. of Michigan (United States)
Dorit D. Adler, Univ. of Michigan (United States)
Mitchell M. Goodsitt, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 2434:
Medical Imaging 1995: Image Processing
Murray H. Loew, Editor(s)

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