Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble
Automatic diagnosis and classification of schizophrenia based on functional magnetic resonance imaging (fMRI) data have attracted increasing attention in recent years. Most previous studies abstracted highly compressed functional features from the view of brain science and fed them into shallow clas...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8790731/ |
| _version_ | 1818947297917009920 |
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| author | Bo Yang Yuan Chen Quan-Ming Shao Rui Yu Wen-Bin Li Guan-Qi Guo Jun-Qiang Jiang Li Pan |
| author_facet | Bo Yang Yuan Chen Quan-Ming Shao Rui Yu Wen-Bin Li Guan-Qi Guo Jun-Qiang Jiang Li Pan |
| author_sort | Bo Yang |
| collection | DOAJ |
| description | Automatic diagnosis and classification of schizophrenia based on functional magnetic resonance imaging (fMRI) data have attracted increasing attention in recent years. Most previous studies abstracted highly compressed functional features from the view of brain science and fed them into shallow classifiers for this purpose. However, their classification performance in practical applications is unstable and unsatisfactory. As an acute psychotic disorder, schizophrenia shows functional complexity in fMRI data. Therefore, additional features and deep classification methods are needed to improve classification performance. In this study, we propose a multiple feature image capsule network ensemble approach for schizophrenia classification. The proposed approach proceeds in three steps: 1) extracting multiple image features from the perspective of linear sparse representation, nonlinear multiple kernel representation, and function connection of brain areas respectively; 2) feeding these image features into three specially designed independent capsule networks for classification; 3) obtaining the final results by fusing the outputs of these three deep capsule network using a ensemble approach. To further improve the classification performance, we design a optimization model of maximizing the square of correlation coefficients and propose a weighted ensemble technology based on this model, which is mathematically proved to be solved as a eigenvalue decomposition problem in certain case. Finally, the proposed approach is implemented and evaluated on the schizophrenia fMRI dataset from COBRE, UCLA and WUSTL. From the experimental results, we conclude that the proposed method outperforms some current methods and further improves the accuracy of schizophrenia classification. |
| first_indexed | 2024-12-20T08:28:40Z |
| format | Article |
| id | doaj.art-8f99c4b6bd624a458b3a26712f90afec |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T08:28:40Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8f99c4b6bd624a458b3a26712f90afec2022-12-21T19:46:45ZengIEEEIEEE Access2169-35362019-01-01710995610996810.1109/ACCESS.2019.29335508790731Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network EnsembleBo Yang0https://orcid.org/0000-0003-4210-8864Yuan Chen1Quan-Ming Shao2Rui Yu3Wen-Bin Li4Guan-Qi Guo5Jun-Qiang Jiang6Li Pan7School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, ChinaSchool of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, ChinaSchool of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, ChinaSchool of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, ChinaSchool of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, ChinaSchool of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, ChinaSchool of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, ChinaSchool of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, ChinaAutomatic diagnosis and classification of schizophrenia based on functional magnetic resonance imaging (fMRI) data have attracted increasing attention in recent years. Most previous studies abstracted highly compressed functional features from the view of brain science and fed them into shallow classifiers for this purpose. However, their classification performance in practical applications is unstable and unsatisfactory. As an acute psychotic disorder, schizophrenia shows functional complexity in fMRI data. Therefore, additional features and deep classification methods are needed to improve classification performance. In this study, we propose a multiple feature image capsule network ensemble approach for schizophrenia classification. The proposed approach proceeds in three steps: 1) extracting multiple image features from the perspective of linear sparse representation, nonlinear multiple kernel representation, and function connection of brain areas respectively; 2) feeding these image features into three specially designed independent capsule networks for classification; 3) obtaining the final results by fusing the outputs of these three deep capsule network using a ensemble approach. To further improve the classification performance, we design a optimization model of maximizing the square of correlation coefficients and propose a weighted ensemble technology based on this model, which is mathematically proved to be solved as a eigenvalue decomposition problem in certain case. Finally, the proposed approach is implemented and evaluated on the schizophrenia fMRI dataset from COBRE, UCLA and WUSTL. From the experimental results, we conclude that the proposed method outperforms some current methods and further improves the accuracy of schizophrenia classification.https://ieeexplore.ieee.org/document/8790731/Schizophrenia classificationmultiple features extractiondeep capsule networkclassifier ensemble |
| spellingShingle | Bo Yang Yuan Chen Quan-Ming Shao Rui Yu Wen-Bin Li Guan-Qi Guo Jun-Qiang Jiang Li Pan Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble IEEE Access Schizophrenia classification multiple features extraction deep capsule network classifier ensemble |
| title | Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble |
| title_full | Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble |
| title_fullStr | Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble |
| title_full_unstemmed | Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble |
| title_short | Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble |
| title_sort | schizophrenia classification using fmri data based on a multiple feature image capsule network ensemble |
| topic | Schizophrenia classification multiple features extraction deep capsule network classifier ensemble |
| url | https://ieeexplore.ieee.org/document/8790731/ |
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