Evidence map›Paper›PMID 40933157›Full record

ArticleWorld journal of psychiatry2025

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

Mehmet Kaan Kaya, Sermal Arslan, Suheda Kaya, Gulay Tasci, Burak Tasci, Filiz Ozsoy, Sengul Dogan, Turker Tuncer

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Article in World journal of psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Mehmet Kaan KayaUniversal Eye Clinic, Elazig 23100, Türkiye.
Sermal ArslanUniversal Eye Clinic, Elazig 23100, Türkiye.
Suheda KayaDepartment of Psychiatry, Elazig Fethi Sekin City Hospital, Elazig 23100, Türkiye.
Gulay TasciDepartment of Psychiatry, Elazig Fethi Sekin City Hospital, Elazig 23100, Türkiye.
Burak TasciVocational School of Technical Sciences, Firat University, Elazig 23100, Türkiye.
Filiz OzsoyDepartment of Psychiatry, Tokat Gaziosmanpasa University, Tokat 60100, Türkiye.
Sengul DoganDepartment of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Türkiye. sdogan@firat.edu.tr.
Turker TuncerDepartment of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOptical 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).

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

methodsA 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).

resultsSelf-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.

conclusionSelf-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.

Indexed as

Biomedical image classificationDeep learning in ophthalmologyOptical coherence tomography image classificationRetinal imaging biomarkersSchizophrenia detectionSelf-AttentionNeXt

Identifiers

PMID40933157
PMCPMC12417991

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