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The purpose of this work is to compare the performance of support vector machines (SVM) and multi-layer perceptron (MLP) in the task of detection and diagnosis of microcalcification clusters in mammograms (MCCs). As data source, the "digital database for screening mammography"; (DDSM) was used. The results show a similar performance for SVM and MLP, in both tasks, detection and diagnosis...
In this work we evaluate the possibilities of Independent Component Analysis (ICA) in conjunction with neural networks with the purpose of finding a set of right parameters which lead to an efficient detection of malignant microcalcification clusters (MCCs) on digitized mammograms. Also, as a part of the ICA process, we aim to state the best window size and component number to be used in the working...
A method is presented for detecting minimally sized microcalcifications on ma mammograms to add extra security to the radiologist's classification. The method imitates the normal procedure followed by the specialist, and is easily implemented on low-cost PCs. As input, it accepts the usual digital mammograms. Tested against one of the most extensive databases - the DDSM of the University of South...
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