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Abstract #0797

Multi-class identification for major depression, bipolar disorder and schizophrenia based on Siamese Network

Chao Li1,2,3, Yue Cui1,2,3, Yongfeng Yang4, Jing Sui1,2,3, Luxian Lv4, and Tianzi Jiang1,2,3
1Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 2National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 3University of Chinese Academy of Sciences, Beijing, China, 4Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China

Siamese Network is an artificial neural network that has been used in small sample sets multi-class classification studies. This study identified major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ) based on combined gray matter, white matter and cerebrospinal fluid using Siamese Network. The participants included four groups: MDD (n = 102), BD (n = 44), SZ (n = 114), and healthy controls (n = 103). We found Siamese Network achieved improved performance than the multilayer perception network with different numbers of features. We achieved a classification accuracy of 46.06% and Macro F1 of 41.47% for this multi-class identification.

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