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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.<\/jats:p>","DOI":"10.3233\/ifs-179387","type":"journal-article","created":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T07:26:13Z","timestamp":1568791573000},"page":"127-137","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Medical brain image classification based on multi-feature fusion of convolutional neural network"],"prefix":"10.1177","volume":"38","author":[{"given":"Dan","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Jilin University, Changchun, China"},{"name":"College of Information Technology and Media, Beihua University, Jilin, China"}]},{"given":"Hongwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Jilin University, Changchun, China"},{"name":"State Key Laboratory of Applied Optics, Changchun, China"},{"name":"Department of Symbolic Computing and Knowledge Engineering, Key Laboratory of the Ministry of Education, Jilin University, Changchun, China"}]},{"given":"Qingliang","family":"Li","sequence":"additional","affiliation":[{"name":"Changchun University of Science and Technology, Changchun, China"},{"name":"Department of Symbolic Computing and Knowledge Engineering, Key Laboratory of the Ministry of Education, Jilin University, Changchun, China"}]}],"member":"179","published-online":{"date-parts":[[2019,9,16]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.canlet.2016.05.033"},{"key":"e_1_3_2_3_2","first-page":"88","article-title":"Research on PET\/CT multimodal image recognition based on convolutional neural network [J]","volume":"41","author":"Yuanyuan W.","year":"2017","unstructured":"W.Yuanyuan, Z.Tao and W.Cuiying, Research on PET\/CT multimodal image recognition based on convolutional neural network [J], Television Technology 41 (2017), 88\u201394.","journal-title":"Television Technology"},{"key":"e_1_3_2_4_2","first-page":"573","article-title":"Study on the method of cerebral vascular extraction based on multimodal convolution neural network [J]","volume":"45","author":"Zhiguang Q.","year":"2016","unstructured":"Q.Zhiguang, C.Hao, D.Yi, et al., Study on the method of cerebral vascular extraction based on multimodal convolution neural network [J], Journal of University of Electronic Science and Technology 45 (2016), 573\u2013581.","journal-title":"Journal of University of Electronic Science and Technology"},{"key":"e_1_3_2_5_2","first-page":"25","article-title":"Study on brain tumor segmentation based on multi-scale convolution neural network [J]","volume":"2","author":"Jian L.","year":"2016","unstructured":"L.Jian, L.Man, L.Xiao, Y.Jiayin, Z.Wenming, L.Yang, et al., Study on brain tumor segmentation based on multi-scale convolution neural network [J], China Medical Equipment 2 (2016), 25\u201328.","journal-title":"China Medical Equipment"},{"key":"e_1_3_2_6_2","first-page":"444","article-title":"Multi-modal emotion recognition based on deep neural network [J]","volume":"33","author":"Jiayin Y.","year":"2017","unstructured":"Y.Jiayin, Z.Wenming, L.Yang, et al., Multi-modal emotion recognition based on deep neural network [J]. 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