Diagnosis of breast cancer in digital mammograms using independent component analysis and neural networks

Authors:
Lúcio F. A. Campos;Aristófanes C. Silva;Allan Kardec Barros
Affiliations:
Laboratory for Biologic Information Processing, University of Maranhão;Laboratory for Biologic Information Processing, University of Maranhão;Laboratory for Biologic Information Processing, University of Maranhão
Venue:
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2005

Citing 4
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Abstract

We propose a method for discrimination and classification of mammograms with benign, malignant and normal tissues using independent component analysis and neural networks. The method was tested for a mammogram set from MIAS database, and multilayer perceptron neural networks, probabilistic neural networks and radial basis function neural networks. The best performance was obtained with probabilistic neural networks, resulting in 97.3% success rate, with 100% of specificity and 96% of sensitivity.