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Noor, M.B.T., et al., 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 informatics, 2020. 7(1): p. 1-21--1-21.","journal-title":"Brain informatics"},{"key":"e_1_3_2_1_6_1","first-page":"3243","volume":"202","author":"Yamanakkanavar N.","unstructured":"Yamanakkanavar , N. , J.Y. Choi , and B. Lee , MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer's disease: a survey. Sensors , 202 0. 20(11): p. 3243 -- 3243 . Yamanakkanavar, N., J.Y. Choi, and B. Lee, MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer's disease: a survey. Sensors, 2020. 20(11): p. 3243--3243.","journal-title":"Sensors"},{"key":"e_1_3_2_1_7_1","volume-title":"International conference on machine learning.","author":"Gupta A.","unstructured":"Gupta , A. , M. Ayhan , and A. Maida . 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Synthesizing Multi-tracer PET Images for Alzheimer's Disease Patients Using a 3D Unified Anatomy-Aware Cyclic Adversarial Network. in International Conference on Medical Image Computing and Computer-Assisted Intervention."},{"key":"e_1_3_2_1_14_1","first-page":"66","volume":"201","author":"Zhang Y.","unstructured":"Zhang , Y. , et al. , Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning. Frontiers in computational neuroscience , 201 5. 9: p. 66 -- 66 . Zhang, Y., et al., Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning. 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