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L. F. A. Campos, A. C. Silva, A. K. Barros

Independent Component Analysis and Neural Networks Applied for Classification of Malignant, Benign and Normal Tissue in Digital Mammography

Keywords: mammogram, breast cancer, independent component analysis, computer-aided diagnosis, multilayer perceptron neural networks

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 forward-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.

Methods of Information in Medicine, Schattauer

Print ISSN: 0026-1270
Volume: 46, 01/2007
Pages: 212 - 215

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