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BACKGROUND Female breast cancer is the major cause of death by cancer in western countries. Efforts in Computer Vision have been made in order to improve the diagnostic accuracy by radiologists. Some methods of lesion diagnosis in mammogram images were developed based in the technique of principal component analysis which has been used in efficient coding(More)
We propose a method for segmentation and classification of breast cancer in digital mammograms using Independent Component Analysis (ICA), Texture Features and Multilayer Perceptron (MLP) Neural Networks. The method was tested for a mammogram set from MIAS database, resulting in 90.15% success rate, with 92% of specificity and 88.3% of sensitivity.
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