Image Classification Combined with Fusion Gaussian–Hermite Moments Feature and Improved Nonlinear SVM Classifier

抄録

With the development of computer technology, data mining, artificial intelligence, and image-processing technology have been applied to medical diagnosis. Image classification is one of the main technologies of medical image processing, which can be used to determine whether a patient suffers from breast cancer according to x-ray images of the breast. To achieve reliable classification of breast images, an image classification method combined with a fusion Gaussian–Hermite moments feature and improved nonlinear support vector machine (SVM) classifier is proposed. The proposed fusion Gaussian–Hermite moments features can improve the robustness and distinguish the ability of features by constructing Gaussian–Hermite invariant moments according to invariant moment theory and constructing a Gaussian–Hermite Fisher moment according to Fisher’s idea. The proposed improved nonlinear SVM classifier can improve the efficiency and accuracy of the classifier through eigen decomposition and sample learning. Experimental results demonstrate that the proposed method has a high accuracy rate for breast x-ray image classification.