Abstract
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 mammograms with a pixel size of 43.5 μm.
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