Classification of Microcalcification of the Diagnosis of Breast Cancer using Artificial Neural Networks.
Annual rept. 1 Sep 94-31 Aug 95,
GEORGETOWN UNIV WASHINGTON DC
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Early detection of breast cancer depends on the accurate classification of microcalcifications. We have developed a computer vision system that can classify microcalcifications objectively and consistently to aid radiologists in the diagnosis of breast cancer. A convolution neural network CNN was employed to classify benign and malignant microcalcifications in the radiographs of pathological specimen. Digital images were acquired by digitizing radiographs at a high resolution of 21 m x 21 m. Eighty regions of interest selected from digitized radiographs of pathological specimen were used for the training and testing of the neural network system. The CNN achieved an Az value of 0.90 in classifying clusters of microcalcifications associated with benign and malignant processes. The classification of microcalcifications for the diagnosis of breast cancer was achieved at a high level in our computer vision system that consists of high resolution digitization of mammograms and a CNN. We have demonstrated the great potential of CNN in classification of microcalcifications for diagnosis of breast cancer.
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