(1) Intensity of relative signals (2) Relative signal intensities and their derivatives in a single vector (3) Maximum signal intensity enhancement, including enhancement time and washout time
MRI
76
(1) Lesions that are both benign and malignant are studied (2) For breast image classification, a linear kernel, a polynomial kernel, and a radial basis function kernel were used in conjunction with the SVM approach
(1) Classification of benign and cancerous images (2) Accuracy, sensitivity, and specificity were attained at 94.94 percent, 92.86 percent, and 93.33 percent, respectively
(1) Images have been classed as benign or cancerous (2) Accuracy, sensitivity, specificity, positive predictive values, and negative predictive values are 85.60 percent, 95.45 percent, 77.86 percent, 77.21 percent, and 95.61 percent, respectively
Chosen feature: Information from the fractional Fourier transform
Mammogram
200
(1) ROI to eliminate unnecessary complexity (2) When SVM and PCA are combined, the accuracy, sensitivity, and specificity reached are percent, %, and %, respectively
(1) ROI was removed to reduce the amount of superfluous complexity (2) Feature reduction: combine SVM and the mixed gravitational search algorithm (MGSA) (3) The acquired accuracy: was 86.00 percent; the SVM+MGSA approach achieved an accuracy: 93.10 percent