Wang, Tian and Bezerianos, Anastasios and Cichocki, Andrzej and Li, Junhua (2022) Multi-Kernel Capsule Network for Schizophrenia Identification. IEEE Transactions on Cybernetics, 52 (6). pp. 4741-4750. DOI https://doi.org/10.1109/TCYB.2020.3035282
Wang, Tian and Bezerianos, Anastasios and Cichocki, Andrzej and Li, Junhua (2022) Multi-Kernel Capsule Network for Schizophrenia Identification. IEEE Transactions on Cybernetics, 52 (6). pp. 4741-4750. DOI https://doi.org/10.1109/TCYB.2020.3035282
Wang, Tian and Bezerianos, Anastasios and Cichocki, Andrzej and Li, Junhua (2022) Multi-Kernel Capsule Network for Schizophrenia Identification. IEEE Transactions on Cybernetics, 52 (6). pp. 4741-4750. DOI https://doi.org/10.1109/TCYB.2020.3035282
Abstract
Schizophrenia seriously affects the quality of life. To date, both simple (e.g., linear discriminant analysis) and complex (e.g., deep neural network) machine learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. To overcome the aforementioned drawbacks, we proposed a multi-kernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match with partition sizes of brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of widely-used dropout strategy in deep learning, we developed capsule dropout in the capsule layer to prevent overfitting of the model. The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. MKCapsnet is promising for schizophrenia identification. Our study first utilized capsule neural network for analyzing functional connectivity of magnetic resonance imaging (MRI) and proposed a novel multi-kernel capsule structure with consideration of brain anatomical parcellation, which could be a new way to reveal brain mechanisms. In addition, we provided useful information in the parameter setting, which is informative for further studies using a capsule network for other neurophysiological signal classification.
Item Type: | Article |
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Uncontrolled Keywords: | Brain connectivity; deep learning (DL); functional magnetic resonance imaging (fMRI); multikernel capsule network (MKCapsnet); schizophrenia diagnosis |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 19 Nov 2020 17:22 |
Last Modified: | 30 Oct 2024 19:35 |
URI: | http://repository.essex.ac.uk/id/eprint/29087 |
Available files
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