Classification of microcalcifications in radiographs of pathological specimen for the diagnosis of breast cancer

Chris Yuzheng Wu; Shih-Chung Benedict Lo; Matthew T. Freedman M.D.; Akira Hasegawa; Rebecca A. Zuurbier M.D.; Seong Ki Mun

doi:10.1117/12.175099

11 May 1994 Classification of microcalcifications in radiographs of pathological specimen for the diagnosis of breast cancer

Chris Yuzheng Wu, Shih-Chung Benedict Lo, Matthew T. Freedman M.D., Akira Hasegawa, Rebecca A. Zuurbier M.D., Seong Ki Mun

Author Affiliations +

Proceedings Volume 2167, Medical Imaging 1994: Image Processing; (1994) https://doi.org/10.1117/12.175099
Event: Medical Imaging 1994, 1994, Newport Beach, CA, United States

Abstract

A convolution neural network (CNN) was employed to classify benign and malignant microcalcifications in the radiographs of pathological specimen. The input signals to the CNN were the pixel values of image blocks centered on each of the suspected microcalcifications. The CNN has been shown to be capable of recognizing different image patterns. Digital images were acquired by digitizing radiographs at a high resolution of 21 micrometers X 21 micrometers . Eighty regions of interest (ROIs) selected from digitized radiographs of pathological specimen were used for the training and testing of the neural network system. The performance of the neural network system was analyzed using the ROC analysis.

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Chris Yuzheng Wu, Shih-Chung Benedict Lo, Matthew T. Freedman M.D., Akira Hasegawa, Rebecca A. Zuurbier M.D., and Seong Ki Mun "Classification of microcalcifications in radiographs of pathological specimen for the diagnosis of breast cancer", Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); https://doi.org/10.1117/12.175099

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