Research Article
A New GLLD Operator for Mass Detection in Digital Mammograms
Table 1
Previously developed approaches on mass detection based on feature extraction and on learning. In this table, we specify for each approach the feature extraction technique, the classifier, the ratio which indicates the number of real masses/number of normal ROIs, and the obtained results. In the feature extraction methods, ICA, PCA, and 2DPCA correspond, respectively, to independent component analysis, principal component analysis and two-dimensional PCA. In the classification stage, ANN, NN, and SVM correspond, respectively, to the artificial neural network, nearest neighbors, and support vector machines. Generally, the evaluation of the works is given in terms of where represents the area under the ROC curve, except for both works of Christoyianni et al. and Leonardo et al. giving the correct classification true positive and true negative in percentage.
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