  Objective: Schizophrenia seriously affects the quality of life. To date, both
simple (linear discriminant analysis) and complex (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. Methods: 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
vector dropout in the capsule layer to prevent overfitting of the model.
Results: 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. Conclusion: MKCapsnet is promising for schizophrenia
identification. Significance: Our study not only proposed a multi-kernel
capsule network but also provided useful information in the parameter setting,
which is informative for further studies using a capsule network for
neurophysiological signal classification.
