Pulmonary nodule benign and malignant identification method based on support vector machine sample reduction

A support vector machine and pulmonary nodule technology, applied in computer parts, character and pattern recognition, instruments, etc., can solve problems such as large space storage requirements, limited application effects, and slow training speed

Inactive Publication Date: 2014-05-07
SHENYANG AEROSPACE UNIVERSITY
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  • Application Information

AI Technical Summary

Problems solved by technology

The essence of support vector machines is to solve mathematical convex quadratic programming problems. When faced with a large number of data samples,

Method used

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  • Pulmonary nodule benign and malignant identification method based on support vector machine sample reduction
  • Pulmonary nodule benign and malignant identification method based on support vector machine sample reduction
  • Pulmonary nodule benign and malignant identification method based on support vector machine sample reduction

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Embodiment Construction

[0048] The detailed structure of the present invention is illustrated in conjunction with examples.

[0049] A method for distinguishing between benign and malignant pulmonary nodules based on support vector machine sample reduction, the specific steps are as follows: Figure 1 to Figure 5 as shown in figure 1 As shown, it is the support vector machine classification diagram of benign and malignant pulmonary nodules in the case of two-dimensional linear separability. The positive sample is benign pulmonary nodules, and the point on the right side of l represents the negative sample (malignant pulmonary nodules) is determined only with the straight line l 1 , l 2 (l 2 is the support vector line of benign pulmonary nodules, l 1 These sample points are called support vectors, and are related to most other sample points, namely l 1 , l 2 The sample points behind the line are irrelevant.

[0050] Step 1: Collect the original sample set S of benign and malignant pulmonary nod...

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Abstract

The invention relates to a pulmonary nodule benign and malignant identification method, and particularly relates to a pulmonary nodule benign and malignant identification method based on support vector machine sample reduction. The method comprises the steps that an original sample set S0 of malignant and benign pulmonary nodules is acquired; sample reduction is carried out for the original sample set S0 of malignant and benign pulmonary nodules, so as to acquire a final train set S2 of malignant and benign pulmonary nodules of a support vector machine; support vector machine train is carried out on the final train set S2 after reduction, so as to acquire a final classification decision function; and support vector machine prediction is carried out on an unknown pulmonary nodule sample xi', so as to acquire a pulmonary nodule benign and malignant identification result. According to the invention, the method of support vector machine sample reduction is provided to improve the train speed of the support vector machine; a space storage requirement is reduced; the pulmonary nodule benign and malignant identification time is reduced; and the diagnosis efficiency and the objective consistency of doctors are improved.

Description

technical field [0001] The invention relates to a method for distinguishing benign and malignant pulmonary nodules, in particular to a method for distinguishing benign and malignant pulmonary nodules based on support vector machine sample reduction. Background technique [0002] At present, lung cancer has become one of the malignant tumors that endanger human health, and the incidence and mortality of lung cancer are still on the rise in recent years. In my country, about 400,000 patients die from lung cancer every year. According to the information provided by the American Cancer Society, in 2006 alone, there were 174,000 new cases of lung cancer in the United States, and 162,000 people died of lung cancer in the same year. However, the current treatment effect of lung cancer is not good, mainly because lung cancer has no obvious clinical symptoms in the early stage, and there is a lack of effective early detection and diagnosis methods. 80% of patients are already in th...

Claims

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Application Information

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IPC IPC(8): G06K9/66
Inventor 郭薇张国栋周炬吴海萍
Owner SHENYANG AEROSPACE UNIVERSITY
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