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Updated: Jul 16, 2026
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
Independent component analysis and neural networks applied for classification of malignant, benign and normal tissue
L F A Campos1, A C Silva, A K Barros
1Laboratory for Biological Information Processing, University Federal of Maranhão, São Luis, Brazil.
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.