[1]A.Fornito,A.Zalesky,C.Pantelis,E.T.Bullmore, (2012).Schizophrenia neuroimaging and connectomes. Neuroimage,62(4), pp.2296-2314
[2] J. Klosterkotter, F. Schultze Lutter, A. Bechdolf, S. Ruhrmann,(2011). Prediction and prevention of Schizophrenia: what has been achieved and where to go next? world Psychiatry,10(3), pp.165-174.
[3] S. R Kay, A. Fiszbein, L.A. Opler, (1987). The positive and negative syndrome scale(PANSS) for schizophrenia. Schizophrenia Bull, 13(2), pp.261-276.
[4] A. Krizhevsky, Sutskever and G.E Hinton, (2017).ImageNet classification with deep convolutional neural network. Communication on the ACM, vol 60, no.6, pp.84-90.
[5] D.T. Schumperle and R. Deriche, (2016). Proceedings of the IEEE Computer Society Conference on computer vision and pattern Recognition. IEEE computer society, vol.1.
[6] K. Simonyan and A. Zisserman, (2014). Very deep convolutional networks for large scale image recognition. Computer Vision and pattern Recognition.
[7] K. He, X. Zhang, S. Ren and J. Sun, (2016). Deep Residual Learning for Image Recognition. IEEE conference on computer vision and pattern recognition(CVPR), pp.770-778.
[8] Yan, Zhicheng, Zhang, Hao, Pramuthu, Robinson, etal.,(2015). HD-CNN: hierarchical deep convolutional neural networks for large scale visual recognition ‘IEEE conference on computer vision (ICCV2015), pp2740-2748, Santigo, Chille
[9] Han, Shaoqiang, Huang, Wei, Zhang, Yan, Zhao, (2017). Recognition of early onset schizophrenia using deep learning method. Applied Informatics Heidel berg, vol.4.Issue1,pp.1-6. [10]D.Sadeghi,A.Shoeibi,N.Ghassemi,P.Khadem,R.Alizadehsani,M.Teshnelab,etal.,(2022). An overview of artificial intelligence techniques for diagnosis of schizophrenia based on magnetic resonance imaging modalities: Methods, challenges and future works. Computational Biol Med.
[11] JinChi Zheng, XiaoLan Wei, Jin Yi Wang, Hua Song Lin, Hong Run pan and YuQing Shi, (2021). Diagnosis of Schizophrenia based on Deep learning using Fmri. Hindawi computational and mathematical methods in medicine,7pages.
[12] J.W. Lai, C.K.E. Ang, U. R Acharya, K.H. Cheong, (2021). Schizophrenia: A survey of artificial Intelligence techniques applied to detection and classification. Int. J. Environ. Res. Public Health, vol.18.
[13] Manan Binth, Taj Noor, Nurat Zerin Zenia, M. Shamim Kaiser, Shamim Ai Manun and Mufti Mahmud, (2020). Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s Disease, Parkinson’s Disease and Schizophrenia. Brain.Inf, pp7-11.
[14] Du Lei, Kun Qin, H.L pinaya, Jonathan Young etal., (2022).Graph convolutional Reveal Network level functional connectivity in schizophrenia. Schizophrenia Bulletin, vol.48 no.4, pp.881-892,
[15] Kang HanOh, Il-seok Oh, Uyanga Tsogt, jie Shen, etal., (2022). Diagnosis of schizophrenia with functional connectome data: a graph based convolutional neural network approach. BMC neuroscience, vol.23, Issue.5.
[16] Yafei Zhu, Shuyue Fu, Shihu Yang, Ping Liang and Ying Tan, (2020). Weighted deep forest for schizophrenia data classification. IEEE Access.
[17] T. Wang, A. Bezerianos, A. Cichoki, J. Li, (2022). Multi kernel Capsule Network for schizophrenia identification. IEEE Trans. Cybern. Jun; 52(6):4741-4750,
[18] B. Yang, Y. Chen, Q.M Shao, R. Yu, W.B.Li, etal.,(2019).Schizophrenia classification using fmri data based on a multiple feature image capsule network ensemble. IEEE access, pp.109956-968.