Classification of microcalcifications in radiographs of pathological specimen for the diagnosis of breast cancer
Chris Y. Wu, Shih‐Chung B. Lo, Matthew T. Freedman, Akira Hasegawa, Rebecca A. Zuurbier, Seong K. Mun · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
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.