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Architectural distortion detection from mammograms using support vector machine

One of the leading diseases in women is breast cancer. The detection in an earlier stage is done by indicating the presence of architectural distortion (AD). An AD detection system with support vector machine is developed in this research. The 15 features are extracted from the fuzzy co-occurrence matrix and fractal dimension. The principal component analysis is also implemented to help in feature redundancy reduction. We found out that the best system for the training data set yields 91.67 % correct AD classification with 0.93 sensitivity of detecting AD and 0.91 specificity of detecting true negative. The best result of the blind test mammograms is at 100.00 % correct AD classification with approximately 16 false positive areas per image.