SVM mammary gland classification method based on Gaussian kernel parameter selection

Abstract

The present invention relates to an SVM mammary gland classification method based on Gaussian kernel parameter selection. The method comprises: extracting a mammary gland molybdenum target and B ultrasonic image features from a known case, and performing benign and malignant and clinical stage mark of each case data which completes the feature extraction according to the known clinical diagnosis result; performing multi-feature fusion of the molybdenum target image features and the B ultrasonic image features of the same patient's mammary gland through adoption of the cascade mode, and obtaining the feature vector of mammary gland samples; and allowing the Gaussian kernel parameter selection method to be used for the training process and the identification process of the dichotomy balance decision tree SVM multi-classification algorithm based on the Gaussian kernel. The accuracy and the efficiency of the breast cancer diagnosis can be improved.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to an SVM mammary gland classification method based on Gaussian kernel parameter selection. Background technique [0002] Breast cancer is one of the most common malignant tumors occurring in women. In recent years, my country's investigations and studies have shown that the incidence of breast cancer is increasing year by year. Therefore, it is more and more meaningful to improve the accuracy of early diagnosis of breast cancer. [0003] At present, the main method used in the diagnosis of breast cancer is through imaging examinations such as mammography and B-ultrasound images, and the diagnoser analyzes the condition through imaging features such as calcification or mass. However, because the density of soft tissue such as glands, blood vessels, and fat in breast tissue is very close to the density of the lesion area, coupled with factors such as visual fatigue...

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Problems solved by technology

However, because the density of soft tissues such as glands, blood vessels, and fat in breast tissue is very close to the density of the lesion area, coupled with factors such as visual fatigue of the diagnostician, misdiagnosis and missed diagnosis of early breast cancer still often occur

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