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

Table 7 Comparative results

Ref.	Model	Dataset	Results, n (%)
He et al[51]	Swin-poly transformer network	OCT2017	Accuracy: 99.80; precision: 99.80; recall: 99.80; F1-score: 99.80; AUC: 99.99
Yoo et al[52]	Few-shot learning, generative adversarial network	OCT2017	Accuracy: 93.90
Huang et al[53]	Novel layer guided CNN	OCT2017	Accuracy: 93.30; sensitivity: 93.30; specificity: 93.30; precision: 91.50
Rajagopalan et al[54]	CNN, Kuan filter	OCT2017	Accuracy: 95.70
Self-AttentionNeXt		OCT2017	Accuracy: 95.87; sensitivity: 95.86; specificity: 98.62; F1-score: 96.25; precision: 95.89

OCT: Optical coherence tomography; AUC: Area under the curve; CNN: Convolutional neural network.