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

Table 1 Characteristics of the collected dataset

Feature	Schizophrenia (n = 67)	Healthy control (n = 46)
Sex (female/male)	15/52	10/36
Mean age, years	Female: 45.10 ± 7.72, male: 41.74 ± 9.00	Female: 30.60 ± 6.45, male: 36.82 ± 5.24
Age range, years	Female: 33-54, male: 18-59	Female: 25-45, male: 20-52
Education, years	Female: 5.90 ± 1.44, male: 17.00 ± 21.01	Female: 8.60 ± 1.25, male: 16.75 ± 3.12

Table 2 Test train image counts in the collected dataset

Diagnosis	Orientation	Train images	Test images	Total images
Schizophrenia	Left to right	1461	416	1877
Healthy control	Left to right	912	260	1172
Schizophrenia	Top to bottom	1461	416	1877
Healthy control	Top to bottom	912	260	1172

Table 3 Sociodemographic information of participants for the Mendeley dataset

Diagnosis	DME	CNV	Drusen	Normal
Number of patients	709	791	713	3548
Mean age, years	57	83	82	60
Age range, years	20-90	58-97	40-95	21-86
Male, n (%)	38.3	54.2	44.4	59.2
Female, n (%)	61.7	45.8	55.6	40.8

CNV: Choroidal neovascularization; DME: Diabetic macular edema.

Table 4 Distribution of the images for the Mendeley dataset

Diagnosis	Train images	Test images	Total
CNV	37213	242	37455
DME	11356	242	11598
Drusen	8624	242	8866
Normal	26323	242	26565

CNV: Choroidal neovascularization; DME: Diabetic macular edema.

Table 5 Test results of our proposed convolutional neural network for our collected dataset, n (%)

Dataset	Class	Accuracy	Sensitivity	Specificity	Precision	F1-score	AUROC
From left to right	Healthy control	97.49	96.92	97.84	96.55	96.74	97.38
	Schizophrenia		97.84	96.92	98.07	97.95	97.38
	Overall		97.38	97.38	97.31	97.35	97.38
From top to bottom	Healthy control	98.96	98.08	99.52	99.22	98.65	98.80
	Schizophrenia		99.52	98.08	98.81	99.16	98.80
	Overall		98.80	98.80	99.02	98.91	98.80

AUROC: Area under the receiver operating characteristic.

Table 6 Test results of the proposed Self-AttentionNeXt for the Mendeley dataset (Optical coherence tomography 2017 dataset), n (%)

Classes	Accuracy	Sensitivity	Specificity	Precision	F1-score	AUROC
CNV	95.87	99.59	95.59	88.28	93.59	97.59
DME		91.32	99.86	99.55	95.26	95.59
Drusen		93.39	100	100	96.58	96.69
Normal		99.17	99.04	97.17	98.16	99.10
Overall		95.87	98.63	96.25	95.90	97.25

CNV: Choroidal neovascularization; DME: Diabetic macular edema; AUROC: Area under the receiver operating characteristic.

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.