Breast cancer image feature selection method based on improved sine and cosine optimization algorithm

Abstract

The invention provides a breast cancer image feature selection method based on an improved sine and cosine optimization algorithm, and the method comprises the steps: extracting feature data of breastcancer image features to acquire a training sample set, and initializing a population; designing a support vector machine classifier according to the training sample set, and performing classification; calculating the fitness value of the current population, and updating related parameters in the policies of the sea squirt and the grey wolf; setting related parameters of a sine and cosine optimization algorithm, and obtaining a population updated through the sine and cosine optimization algorithm; updating the obtained populations updated by the sine and cosine optimization algorithm througha sea squirt flight strategy, a grey wolf flight strategy and a Levy flight strategy to obtain three populations; screening out an optimal population through greedy selection; and if the termination condition is met, ending and outputting the optimal solution, otherwise, continuing iteration until the iterative computation is ended. By implementing the method, the problems of falling into a localoptimal solution, low convergence rate and the like of a sine and cosine optimization algorithm can be solved, and classification and prediction of breast cancer images are realized.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to a breast cancer image feature selection method based on an improved sine-cosine optimization algorithm. Background technique [0002] In recent years, with the increasing morbidity and mortality, cancer has become one of the important factors affecting human health. Breast cancer, as the most common malignant tumor in women's diseases, seriously endangers women's health. In the context of current medical equipment and medical technology, early diagnosis and early treatment have become the key means to treat breast cancer. With the continuous development of machine learning technology, machine learning algorithms can detect the risk of cancer in a simpler and more effective way, so as to achieve the purpose of reducing the incidence of cancer. Image-based identification of benign and malignant tumors is of great significance for the early diagnosis and identifi...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the embodiments of the present invention is to provide a breast cancer image feature selection method based on the improved sine-cosine optimization alg

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