High-Dimensional Feature Selection Algorithm Based on Bayesian Rough Sets and Cuckoo Algorithm

Abstract

The invention discloses a high-dimensional feature selection algorithm based on Bayesian rough set and cuckoo algorithm, comprising: acquiring a lung tumor image, and performing target contour segmentation to obtain a segmented ROI image; The high-dimensional feature components of , and the decision information table containing feature attributes is constructed based on the feature components; the BRSGA algorithm is used to reduce the original feature space to obtain the optimal feature subset, and the CS algorithm is used to carry out the penalty factor and kernel function parameters of the SVM. optimization, and input the reduced feature subset into the optimized SVM to obtain the classification and recognition results. The invention generates the optimal feature subset through genetic algorithm and BRS, reduces the feature dimension without reducing the classification accuracy, gets rid of the constraints of manual parameter setting, and reduces time consumption. Using CS to optimize the SVM parameters globally has a more effective exploration search space, enriches the diversity of the population, and has good robustness and strong global search ability.

Description

technical field [0001] The invention relates to the technical field of medical image recognition, in particular to a high-dimensional feature selection algorithm based on Bayesian rough set and cuckoo algorithm. Background technique [0002] With the development of computer aided diagnosis (CAD) research, medical image processing technology has developed rapidly. However, the multi-modality, grayscale ambiguity and uncertainty of medical images make the rate of missed diagnosis and misdiagnosis remain high in the process of single-modality medical imaging diagnosis. Therefore, different modalities of medical image processing technology emerge as the times require, which are divided into pixel level, feature level and decision level according to different levels. The feature-level processing can compress the amount of information on the basis of retaining important information, and the processing speed is faster. In the process of feature-level processing of medical images,...

AI Technical Summary

Problems solved by technology

Many studies on BRS are still in the stage of theoretical analysis, lack of mature and independent models, and have not been combined with other algorithms to deal with the problem of high-dimensional feature selection of medical images.

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