Affiliations: [a] Department of Computer Science and Technology, Jilin University, Changchun, China
| [b] College of Information Technology and Media, Beihua University, Jilin, China
| [c] State Key Laboratory of Applied Optics, Changchun, China
| [d]
Changchun University of Science and Technology, Changchun, China
| [e] Department of Symbolic Computing and Knowledge Engineering, Key Laboratory of the Ministry of Education, Jilin University, Changchun, China
Correspondence:
[*]
Corresponding author. Hongwei Zhao, Department of computer science and technology, Jilin University, Changchun, China. E-mail: wangdanjl_dx@163.com.
Abstract: This paper presents a medical brain image algorithm based on multi-feature fusion. Feature extraction based on convolutional neural network was used as texture information, feature extraction based on voxel information was used as morphological feature, and then the two types of features were combined in series. Feature extraction based on convolutional neural network was used as texture information, feature extraction based on voxel information was used as morphological feature, and then the two types of features were combined in series. Then the heuristic search algorithm is used to optimize the feature selection stage. Based on the feature score table extracted by the recursive feature elimination method of support vector machine, the correlation between features is added. Moreover, through experimental analysis, the optimal value of the parameter K was selected according to the heuristic search, and the optimal feature subset was extracted after determining the value of the parameter K. Experiments show that compared with similar algorithms, this algorithm improves the accuracy and efficiency of the classification of brain images.
