Image classification using artifical neural networks

Berkman Sahiner; Heang-Ping Chan; Nicholas Petrick; Datong Wei; Mark A. Helvie M.D.; Dorit D. Adler; Mitchell M. Goodsitt

doi:10.1117/12.208758

12 May 1995 Image classification using artifical neural networks

Berkman Sahiner, Heang-Ping Chan, Nicholas Petrick, Datong Wei, Mark A. Helvie M.D., Dorit D. Adler, Mitchell M. Goodsitt

Author Affiliations +

Proceedings Volume 2434, Medical Imaging 1995: Image Processing; (1995) https://doi.org/10.1117/12.208758
Event: Medical Imaging 1995, 1995, San Diego, CA, United States

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.

Citation Download Citation

Berkman Sahiner, Heang-Ping Chan, Nicholas Petrick, Datong Wei, Mark A. Helvie M.D., Dorit D. Adler, and Mitchell M. Goodsitt "Image classification using artifical neural networks", Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); https://doi.org/10.1117/12.208758

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