Computer-aided mammary gland diagnosing method by means of characteristic weight adaptive selection

Abstract

The invention relates to a computer-aided mammy gland diagnosing method by means of characteristic weight adaptive selection. The method comprises the steps of firstly extracting a mammary gland X-ray molybdenum target and a type-B ultrasonic image data characteristic, performing benign-malignant and clinic periodic marking on case data after characteristic extraction according to known clinical diagnosis results; performing multi-characteristic fusion on the X-ray molybdenum target image and the type-B ultrasonic image of the mammary gland of a same patient according to a cascaded manner, and obtaining the characteristic vector of a mammary gland sample; afterwards using a characteristic weight adaptive selection method on the training process of a binary balanced decision tree SVM multi-class classification algorithm based on a Gaussian kernel; and finally utilizing the characteristic weight adaptive selection method on the identification process of the binary balanced decision tree SVM multi-class classification algorithm based on the Gaussian kernel. The computer-aided mammy gland diagnosing method can improve accuracy and efficiency in breast cancer diagnosis.

Description

technical field [0001] The invention relates to the technical field of feature engineering, in particular to a method for computer-aided mammary gland diagnosis using feature weight self-adaptive 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 mammary X-rays 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 ...

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Problems solved by technology

The performance of the SVM classifier using the above method can be improved to a certain extent, but if the "hard selection" is applied to the SVM multi-classification algorithm of the binary balanced decision tree, there are two problems: first, for the binary balanced decision tree SVM For each decision-making surface of the multi-classification algorithm, the important feature dimensions are different, and the feature dimensions extracted by the "hard selection" method are not suitable for each decision-making surface; second, the selected feature dimensions participate in the calculation with the same importance. It cannot more accurately reflect the importance of the feature dimensions of different decision-making surfaces, so that the performance of the binary balanced decision tree SVM multi-classification algorithm cannot be guaranteed, so the "hard selection" method is not suitable for the SVM multi-classification algorithm of the binary balanced decision tree

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