JACIII Vol.22 No.6 pp. 875-882
doi: 10.20965/jaciii.2018.p0875


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

Li Wan

School of Electronics and Information Engineering, Hunan University of Science and Engineering
Building 130, Yangzitang Road, Lingling District, Yongzhou City, Hunan 425199, China

October 5, 2017
July 2, 2018
October 20, 2018
Gaussian–Hermite moments, support vector machines, medical image classification computer-aided diagnosis, imaging diagnosis
Image Classification Combined with Fusion Gaussian–Hermite Moments Feature and Improved Nonlinear SVM Classifier

The framework of the proposed method

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.

Cite this article as:
L. Wan, “Image Classification Combined with Fusion Gaussian–Hermite Moments Feature and Improved Nonlinear SVM Classifier,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.6, pp. 875-882, 2018.
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Last updated on Nov. 12, 2018