Schizophrenia is a debilitating, chronic mental disorder that significantly
impacts an individual's cognitive abilities, behavior, and social interactions.
It is characterized by subtle morphological changes in the brain, particularly
in the gray matter. These changes are often imperceptible through manual
observation, demanding an automated approach to diagnosis. This study
introduces a deep learning methodology for the classification of individuals
with Schizophrenia. We achieve this by implementing a diversified attention
mechanism known as Spatial Sequence Attention (SSA) which is designed to
extract and emphasize significant feature representations from structural MRI
(sMRI). Initially, we employ the transfer learning paradigm by leveraging
pre-trained DenseNet to extract initial feature maps from the final
convolutional block which contains morphological alterations associated with
Schizophrenia. These features are further processed by the proposed SSA to
capture and emphasize intricate spatial interactions and relationships across
volumes within the brain. Our experimental studies conducted on a clinical
dataset have revealed that the proposed attention mechanism outperforms the
existing Squeeze & Excitation Network for Schizophrenia classification.

该研究引入了一种深度学习方法，使用空间序列注意力（SSA）机制对带有精神分裂症的个体进行分类，通过结构性磁共振成像（sMRI）提取并强调重要特征表征，实验证明该注意力机制在精神分裂症分类中优于现有的压缩和激励网络。