Self-AttentionNeXt: Exploring schizophrenic optical coherence tomography image detection investigations.

BACKGROUND

Optical coherence tomography (OCT) enables high-resolution, non-invasive visualization of retinal structures. Recent evidence suggests that retinal layer alterations may reflect central nervous system changes associated with psychiatric disorders such as schizophrenia (SZ).

AIM

To develop an advanced deep learning model to classify OCT images and distinguish patients with SZ from healthy controls using retinal biomarkers.

METHODS

A novel convolutional neural network, Self-AttentionNeXt, was designed by integrating grouped self-attention mechanisms, residual and inverted bottleneck blocks, and a final 1 × 1 convolution for feature refinement. The model was trained and tested on both a custom OCT dataset collected from patients with SZ and a publicly available OCT dataset (OCT2017).

RESULTS

Self-AttentionNeXt achieved 97.0% accuracy on the collected SZ OCT dataset and over 95% accuracy on the public OCT2017 dataset. Gradient-weighted class activation mapping visualizations confirmed the model's attention to clinically relevant retinal regions, suggesting effective feature localization.

CONCLUSION

Self-AttentionNeXt effectively combines transformer-inspired attention mechanisms with convolutional neural networks architecture to support the early and accurate detection of SZ using OCT images. This approach offers a promising direction for artificial intelligence-assisted psychiatric diagnostics and clinical decision support.