Intensity information Value of detected corner Energy
Mammogram
600
Classified images into normal and abnormal images. Removing unwanted objects from the images for reducing the redundancy and computational complexity. Achieved Precision and Recall rates are 96.80% and 92.5%, respectively.
Shape, fat, presence of calcification texture, spiculation, Contrast, Isodensity type features selected Total number of features 181
Mammogram
1200
Features have been selected from the region of interest. They utilized the radial basis function (RBF) for their analysis. The Sequential Forward Floating Selection (SFFS) method utilized for the feature selection. The area under the receiver operating characteristic curve was (AUC) = .
Histogram of the intensity has been used as a statistical feature. 2D Gabor filter utilized for the textural feature extraction Clinical features extracted from the database directly
Mammogram
322
When using SVM with the linear kernel the obtained Accuracy, Sensitivity, and Specificity are 98%, 100%, and 96%, respectively. When using weighted feature SVM with weights the obtained Accuracy, Sensitivity, and Specificity are 90%, 100% and 75%, respectively.