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- W2078649369 abstract "In this paper, we propose a new region-based method for detecting mass tumors in digital mammograms. Our method uses principal component analysis (PCA) techniques to reduce the image data into a subspace with significantly reduced dimensionality using an optimal linear transformation. After the transformation, classification in the subspace is performed using a nearest neighbor classifier. We consider the detection of only mass abnormalities in this study. Micro calcifications, spiculated lesions, and other abnormalities are not considered. We implemented our method and achieved a 93% correct detection rate for mass abnormalities in our tests." @default.
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- W2078649369 date "2005-03-11" @default.
- W2078649369 modified "2023-09-27" @default.
- W2078649369 title "Detection of mass tumors in mammograms using SVD subspace analysis" @default.
- W2078649369 doi "https://doi.org/10.1117/12.596943" @default.
- W2078649369 hasPublicationYear "2005" @default.
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