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

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...

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, the training speed is very slow, and the space storage requirements are large. These shortcomings will limit its application effect in various fields.

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