29 July 1993 Comparative evaluation of pattern recognition techniques for detection of microcalcifications
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Computer detection of microcalcifications in mammographic images will likely require a multi-stage algorithm that includes segmentation of possible microcalcifications, pattern recognition techniques to classify the segmented objects, a method to determine if a cluster of calcifications exists, and possibly a method to determine the probability of malignancy. This paper will focus on the classification of segmented objects as being either (1) microcalcifications or (2) non-microcalcifications. Six classifiers (2 Bayesian, 2 dynamic neural networks, a standard backpropagation network, and a K-nearest neighbor) are compared. Methods of segmentation and feature selection are described, although they are not the primary concern of this paper. A database of digitized film mammograms is used for training and testing. Detection accuracy is compared across the six methods.
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Kevin S. Woods, Kevin S. Woods, Jeffrey L. Solka, Jeffrey L. Solka, Carey E. Priebe, Carey E. Priebe, Chris C. Doss, Chris C. Doss, Kevin W. Bowyer, Kevin W. Bowyer, Laurence P. Clarke, Laurence P. Clarke, } "Comparative evaluation of pattern recognition techniques for detection of microcalcifications", Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); doi: 10.1117/12.148696; https://doi.org/10.1117/12.148696

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