Research Area: Machine Learning
Alzheimer disease is a neurodegenerative brain disorder leading to gradual loss of memory. Multiple biomarkers have been accepted for identifying the Alzheimers disease namely Neuroimaging, Cerebrospinal fluid proteins, blood and urine tests, genetic risk profilers. In this study, an extensive review has been done for Alzheimer disease prediction using diverse brain-imaging biomarkers through varied deep learning frameworks. A closer look revealed that taking into account multiple modalities of neuroimaging biomarkers always lead to better prediction accuracy for multi-class classification of Alzheimer disease. The paper further discusses about multiple open areas that need to be drilled down for establishing a model that can be accepted by medical community for Alzheimer prediction. This review work explores the different dimensions of neuroanatomical approach on which different deep learning frameworks that can be applied since the performance of designed model using 3-D subject-level, 3-D ROI-based and 3-D patch-level approaches varies. There is a need of extensive analysis for suitability of these methods for particular type of model.
Keywords:
Neuroimaging
Alzheimer disease
Deep learning
Convolutional networks
Author(s) Name: Nitika Goenka, Shamik Tiwari
Journal name: Artificial Intelligence Review
Conferrence name:
Publisher name: Springer
DOI: 10.1007/s10462-021-10016-0
Volume Information: 54, pages 4827–4871 (2021)
