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. 2023 Sep 2;13:14433. doi: 10.1038/s41598-023-41359-z

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Quantitative performance comparisons of our model and the benchmark model. (A) Receiver operating characteristics (ROC) curves for schizophrenia classification on the dataset. The green line represents the ROC curve of the benchmark model with the input of T1W WH scans. The red line represents the ROC curve of the SE-VGG-11BN with the input of T1W WB scans. (B) Bar plot of the classification performance of these models in terms of the accuracy (at the default operating threshold of 0.5, Th = 0.5), sensitivity, specificity, and the area under the ROC curve (AUC). (C) Table quantitatively summarizes the performance of these models. The p-value of the ROC test (DeLong’s test) indicated our model is significantly better than the benchmark model at a level of 0.05. (D) Generalizability of the two models trained by COBRE and NMorphCH datasets on unseen BrainGluSchi test dataset. Considering components A through D, SE-VGG-11BN exhibits a significantly better performance than the benchmark model.