Independent Component Analysis and Neural Networks Applied for Classification of Malignant, Benign and Normal Tissue in Digital Mammography - Campos, L. F. A.; Silva, A. C.; Barros, A. K. (Methods of Information in Medicine, 2007)
Objectives: This paper proposes an efficient method for the discrimination and classification of mammograms with benign, malignant and normal tissues.Methods: The proposed method consists of selection of tissues, feature extraction using independent component analysis, feature selection by the foiward- selection technique and classification of the tissue by the multilayer perceptron.Results: The method is tested for a mammogram set of the MIAS database, resulting in a 97.83% success rate, with 98.0% specificity and 97.5% sensitivity.Conclusion: The proposed method showed a good classification rate. The method will be useful for early cancer diagnosis.
Keywords: Mammogram; breast cancer; independent component analysis; computer-aided diagnosis; multilayer perceptron neural networks